Program 14 · Domain specialisation

SCIKIQ Certified Data and AI EngineerTelecom Towers

A tower company is not a small telco. It is a landlord with an energy problem: revenue is tenancy ratio times lease rate, cost is overwhelmingly power and site maintenance, and the contract is an uptime SLA with money attached. Confusing the two businesses is the most common reason AI work in this sector solves the wrong problem well.

80 hrstaught
40hands-on AI labs
8assessment tests
+20 hrs2 E2E capstones

Talk to us about a cohort See the assessments

Tenancy ratioThe single number the entire business model turns on

EnergyThe dominant controllable cost line at almost every site

Uptime SLAWhere operational failure converts directly into penalty

Multi-tenantCompeting operators on one asset, and data that must not cross

Part 1 · The business

The value chain, the people, the trends

The tower business has an unusual shape: a few large customers, tens of thousands of unmanned remote assets, and a cost base dominated by diesel and grid. Part 1 maps it and names the people who run it.

The tower value chain

Six stages from site acquisition to portfolio. Select one.

Who you are building for

Almost every asset is unmanned and remote, so the people here manage by exception and by evidence. Select one to light the stages they own.

What the sector is seeing

A capital-intensive landlord business where the operational data is finally good enough to act on.

IndustryPower-led economics

Energy is the value pool

Power dominates the controllable cost base at most sites, which makes consumption anomaly detection the highest-return analytics work in the sector.

IndustryCo-location compounds

Tenancy ratio drives everything

Incremental tenants land on an already-built asset, so anything that finds or unblocks a co-location compounds directly into margin.

IndustryEstate-wide telemetry

Remote monitoring finally covers the estate

Site controllers and remote monitoring now cover enough of the portfolio that failure prediction stops being a pilot on twenty sites.

IndustryEvidence wins deductions

SLA disputes are an evidence problem

Outage exclusion is argued monthly between towerco and operator, and the party with the better-assembled evidence wins the deduction.

IndustryPopulation-level diligence

Lease portfolios are the hidden liability

Ground-lease obligations across thousands of scanned agreements are rarely known at population level, which makes diligence and escalation exposure a document problem.

IndustrySite-level, not average

Renewables shifted from ESG to economics

Solar and battery investment is now justified on fuel displacement at specific sites, which requires site-level behaviour rather than portfolio averages.

Part 2 · Data and meaning

Domains, ontology, taxonomy and governed terms

This estate is unusual: tens of thousands of unmanned assets streaming telemetry, a small number of very large customers, and a document corpus — leases and permits — that carries most of the commercial risk. The architecture has to serve all three.

The data landscape

Six families. Note that two of them, leases and permits, are almost entirely scanned documents.

Site & asset register

Towers, structures, shelters, equipment and their configuration.

Asset registers, GIS

Remote monitoring

Power, alarms, access, temperature and generator telemetry per site.

RMS platforms, site controllers

Energy

Grid readings, diesel deliveries and consumption, battery and solar generation.

Fuel management, meters, RMS

Leases & permits

Ground leases, tenant agreements, amendments and regulatory permits.

Contract stores, scanned agreements

Work orders & field

Preventive and corrective jobs, spares, crews and site visits.

FSM, CMMS, ticketing

SLA & billing

Uptime measurement, outage classification, invoices and deductions.

SLA engines, billing systems

The semantic layer

A tower agent that cannot separate a site from a structure, or a tenant from a lease, will produce answers that are commercially wrong in a business where the contract is the product. These are the domains, the graph and the definitions it must be grounded on.

Data domains

Six subject areas, each with the entities it holds, its critical data elements and the function accountable for it.

Site & structure

The physical estate: land, tower, shelter and the equipment on it.

SiteStructureShelterEquipment

Critical data elements: Site identifier; Structure type and height; Load capacity
Owned by: Asset management

Tenancy

Who occupies the structure, under what terms, at what load.

TenantTenancyAmendmentLoading

Critical data elements: Tenant identifier; Lease rate; Occupied load
Owned by: Commercial

Land & permits

The obligations under the asset: ground leases and regulatory consent.

Ground leaseLandlordPermitEasement

Critical data elements: Lease term; Escalation rate; Permit expiry
Owned by: Site acquisition & legal

Energy

How the site is powered, and what that power costs.

Grid supplyGeneratorBatterySolar arrayFuel delivery

Critical data elements: Consumption; Generator runtime; Fuel litres delivered
Owned by: Energy management

Operations

What was monitored, what broke and who was sent.

AlarmOutageWork orderSite visit

Critical data elements: Outage duration; Fault code; First-time fix flag
Owned by: O&M

Service level

The uptime commitment made to each tenant and the money attached to it.

SLAUptime measurementExclusionPenalty

Critical data elements: Committed uptime; Excludable minutes; Penalty amount
Owned by: Commercial & SLA

Ontology & knowledge graph

The entities and the typed relationships between them — what the agent traverses instead of guessing joins. Select any entity.

Core entityEventReference / classification

Taxonomies

The classification hierarchies that make records comparable across systems.

Asset hierarchy

Region
Cluster
Site
Structure
Equipment

North → Cluster 12 → Site 8841 → Lattice 40m → Rectifier 2

Outage cause

Class
Cause
Excludable

Power → Grid failure beyond backup → Excludable under tenancy terms

Energy source

Type
Source
Mode

Hybrid → Grid + battery → Generator on grid failure

Governed business terms

The definitions an agent must use rather than invent. Most wrong answers in this industry are a term used loosely.

Site vs structure: A site is the location and its obligations; a structure is what tenants mount to. One site can carry more than one structure, and the lease attaches to the site.

Tenancy ratio: Average tenants per structure. The business model turns on it, because an added tenant lands on capital already spent.

Excludable outage: Downtime the tenancy agreement removes from the SLA calculation. The definition differs per tenant, so it is contractual before it is technical.

Uptime: Service availability at the site as the tenancy defines it — not the same as telemetry being present.

Telemetry gap: A period with no monitoring data. It is unknown, not uptime, and treating it as uptime overstates SLA compliance.

Escalation clause: The contractual mechanism increasing ground rent over the lease term, and the main long-term cost driver.

Pilferage: Fuel leaving a site by means other than consumption, visible only as a reconciliation gap.

Preventive mix: The share of maintenance that was planned rather than reactive — the cost lever O&M is judged on.

Part 2 · The architecture

How it is actually built

The layers a deployment needs, what each one holds, and what the engineer owns there.

The reference architecture

Seven layers, with tenant data segregation treated as a hard architectural boundary.

Typical tech stack

Models

Frontier models for lease and permit reasoning
Time-series anomaly models on power
Vision models for site inspection imagery

Grounding

Lease and permit clause index
Standards and precedent corpus
Site knowledge graph

Agents

Predictive maintenance agents
MCP over RMS and field service
SLA pack assembly

Data

Telemetry ingestion with gap handling
Fuel and meter reconciliation
Scanned document extraction

Assurance

Prediction accuracy evals
Tenant isolation testing
Energy anomaly precision

Runtime

Central deployment with edge tolerance
Offline-capable field tooling
Per-tenant reporting isolation

Constraints that shape the design

Tenants are competitors

Two rival operators share one tower. Their traffic, outage and configuration data must be provably separated, and that separation is architectural rather than procedural.

Monitoring goes dark

Remote sites lose connectivity. A gap in telemetry is not evidence of uptime, and any system that treats it as such will overstate SLA compliance.

The commercial risk is in scanned paper

Ground leases, escalation clauses and permits are images. Extraction quality is the ceiling on every commercial use case here.

A dispatch is a long drive

Sites are remote and crews are scarce. False positives cost a day, not an hour, so precision matters more than recall.

Part 3 · Getting it done

Where the business and the technology meet

The use cases split cleanly in two: operational ones driven by telemetry, and commercial ones driven by documents. Most towercos underinvest badly in the second.

Use cases, mapped to the chain and the architecture

Filter by stage or by earned autonomy. Selecting a use case jumps the value chain and the architecture to the stage and layer it depends on.

Value chain stage

Autonomy earned

12 use cases

The delivery playbook

How a towerco deployment actually runs.

Week 1 — reconcile the register to reality

Establish how far the asset register differs from what is physically on site. Every telemetry and economics use case inherits this error, so measure it before promising anything.

Week 2 — settle tenant segregation

Agree and architecturally enforce how competing tenants are separated. In this sector it is a commercial precondition, not a security nicety, and it cannot be retrofitted.

Weeks 3-6 — start with energy, not with prediction

Consumption reconciliation needs no model risk appetite, attacks the largest cost line and produces a number finance recognises within weeks.

Weeks 7-9 — take on the document population

Lease and permit extraction is where the commercial upside sits. Measure clause-level accuracy on a lawyer-labelled sample before anyone relies on the output.

Weeks 10-12 — prediction, dispatch and handover

Introduce failure prediction with false-dispatch tracking from day one, then hand over runbook, thresholds and the segregation evidence.

The curriculum

8 modules, 80 taught hours, 40 hands-on labs and 8 assessments — every lab provisioned and graded by the SCIKIQ Agentic AI Playground. Open a module to see its labs.

01The SCDAI delivery model in infrastructureLanding in a landlord business whose customers are a handful of large, demanding tenants.10 hrs · 5 labs

Delivery mandateTowerco vs operator economicsValue sizing on energy and SLAStakeholder mapTenant sensitivityThin-slice scoping

Decision selectionScore four towerco decisions on value, cadence and data readiness.

Value sizingSize a litre of diesel, an SLA penalty point and a co-location.

Register auditAssess whether the asset register supports a site-level use case.

Stakeholder mapMap O&M, energy, commercial, site acquisition and portfolio.

Thin sliceScope a single-region energy slice with a measurable outcome.

Assessment 01 — Operating model & scoping25 items · 35 min · pass 70%

02Infrastructure data foundationsRegisters, telemetry, fuel, work orders and a very large scanned contract corpus.10 hrs · 5 labs

Asset registersRemote monitoring dataEnergy and fuel dataWork-order dataScanned lease corporaTelemetry gaps

Register reconciliationReconcile the asset register against telemetry evidence.

Telemetry ingestionIngest site telemetry and mark gaps explicitly as unknown.

Fuel reconciliationReconcile deliveries, runtime and load at site level.

Work-order parsingExtract failure modes from free-text field reports.

Document ingestionIngest scanned leases with per-page OCR quality scoring.

Assessment 02 — Infrastructure data landscape25 items · 35 min · pass 70%

03Context engineering for contracts and sitesClause language, site identifiers, units of energy and the precision each demands.10 hrs · 5 labs

Site identifier resolutionClause languageUnits and conversionsStructured outputsUncertainty on poor scansContext budget

Site resolutionResolve ambiguous site references across register and telemetry.

Clause extraction contractEmit escalation terms as a typed, validated structure.

Unit safetyPrevent energy unit-conversion errors in extracted values.

Scan uncertaintyReturn reduced confidence when OCR quality is poor, not a guess.

Context budgetCut context cost on a portfolio-wide extraction run.

Assessment 03 — Context engineering25 items · 35 min · pass 70%

04Grounding on leases, permits and standardsThe commercial risk in this business is written down, scanned, and unsearchable.10 hrs · 5 labs

Clause-level retrievalPermit precedentAmendment chainsSite graph traversalCitationsRetrieval evals

Clause indexBuild clause-level retrieval over a scanned lease population.

Amendment chainsResolve the operative term through a chain of amendments.

Site traversalTraverse site to structure to tenant to lease obligation.

Citation contractCite document, page and clause behind every extracted term.

Retrieval evalsBuild a lawyer-labelled clause set and measure precision and recall.

Assessment 04 — Retrieval & grounding25 items · 40 min · pass 70%

05Operational agent designPrediction, energy anomaly and the dispatch decision on a long drive.10 hrs · 5 labs

Workflow vs agentFailure prediction patternsAnomaly vs accusationDispatch decisioningPrecision over recallEscalation

Prediction agentPredict generator failure from telemetry and maintenance history.

Energy anomalyFlag sites where fuel, runtime and load do not reconcile.

Evidence framingPresent a loss anomaly as evidence rather than as an allegation.

Dispatch triageDecide dispatch and recommend parts, degrading safely when unsure.

EscalationDefine when a finding must go to a human investigator and prove it does.

Assessment 05 — Agent design for operations25 items · 40 min · pass 70%

06Integrating with RMS, field service and billingTelemetry in, work orders out, SLA packs assembled and delivered per tenant.10 hrs · 5 labs

MCP over RMS and FSMWork-order write-backReport generationOffline field toolingIdentityError handling

MCP over RMSExpose site telemetry and status as typed read tools.

Work-order write-backRaise and update jobs under a traceable service identity.

SLA packGenerate a per-tenant monthly pack with evidence attached.

Offline behaviourSupport a field tool that loses connectivity mid-job.

IdentityBind agent access to region, tenant and role entitlements.

Assessment 06 — Systems integration25 items · 35 min · pass 70%

07Tenant segregation, security and commercial riskThe module that decides whether tenants will let you near shared-asset data at all.10 hrs · 5 labs

Tenant isolationPurpose limitationConfidentiality obligationsOWASP LLM Top 10Injection via scanned documentsIncident response

Isolation designArchitect per-tenant isolation across index, model and reporting.

Isolation testingAttempt cross-tenant retrieval and prove it fails.

Confidentiality reviewMap lease confidentiality obligations to system controls.

Injection defenceDefend against instructions embedded in a scanned agreement.

Red teamRun an OWASP LLM Top 10 pass over the extraction pipeline.

Assessment 07 — Segregation, security & risk30 items · 45 min · pass 70%

08Production, false dispatch and cost per sitePrecision is the operative metric when a false positive costs a day of driving.10 hrs · 5 labs

Eval suitesFalse-dispatch measurementDriftThreshold tuningCost per siteHandover

Eval gateGate deployment on prediction precision regression.

False-dispatch studyMeasure and reduce false dispatch with field operations.

Drift watchDetect drift after a generator fleet or contractor change.

Cost per siteModel and reduce inference cost across the site portfolio.

HandoverProduce runbook, threshold guide and tenant-segregation evidence.

Assessment 08 — Production & economics30 items · 45 min · pass 70%

Two capstones — 20 hours

End-to-end agent design, development, deployment and testing, 10 hours each. Reviewed by a Senior SCDAI Engineer against a published rubric.

Capstone A — Energy anomaly agent

Build an agent that reconciles fuel delivered, generator runtime and site load, flags the sites that do not add up, and presents each finding as evidence a human investigator can act on rather than as an accusation. Measure precision on a labelled site set.

Capstone B — Lease obligation agent

Build clause-level retrieval and extraction over a scanned lease population that returns term, rent, escalation and assignment provisions with document, page and clause cited, resolved correctly through amendment chains. Measure against a lawyer-labelled set.

What you deliver, and how it is marked

A running agent, deployed on the program's target stack, reachable by a reviewer.
An architecture note: boundaries, tools, autonomy level and the trade-offs you took.
A versioned eval set with a baseline and your final scores.
A trace walkthrough of one success and one deliberate failure.
Cost per task, measured — not estimated.
A runbook and rollback path a client team could operate on Monday.

Problem fit15%Is this a decision worth automating, scoped to a defensible boundary?

Architecture20%Right pattern for the constraints; autonomy earned, not assumed.

Grounding & accuracy20%Measured groundedness against the eval set, with the failures named.

Safety & compliance20%Domain controls actually enforced, and demonstrably so.

Operability15%Traces, runbook, rollback, cost — could someone else run this?

Communication10%Can a client executive follow the reasoning in ten minutes?

One lab, start to finish

Every lab in this program follows the shape below. This is lab 05 in full — the brief you are given, the environment that is provisioned for you, the code you start from and the assertions that decide whether you passed.

Module 05 · Operational agent design

Fuel, runtime and load reconciliation

Find the sites where diesel delivered cannot be explained by what the site actually did.

The brief

You are given twelve months of fuel deliveries, generator runtime, grid availability and site load across 300 sites, plus a labelled set of sites with known causes. Some gaps are metering error, some are genuine inefficiency, some are losses. Return the sites that do not reconcile, with the evidence for each — and without asserting which cause applies, because this data cannot distinguish them.

Provisioned for you

300 sites, 12 months of deliveries and runtime
Grid availability with realistic telemetry gaps
Site load profiles
Labelled key: 14 sites, causes recorded separately

You start from this

def reconcile(site_id, deliveries, runtime, grid, load):
    """Return (expected_litres, actual_litres, gap, evidence) for one site.

    Telemetry gaps are unknown, not zero. A site that went dark for a week
    has not proven it consumed nothing.
    """
    # 1. expected burn = runtime x load-adjusted consumption rate
    # 2. exclude no-telemetry periods from BOTH sides of the comparison
    # 3. evidence must let a human reach their own conclusion
    raise NotImplementedError

What the grader asserts

Finds at least 12 of the 14 labelled sites
Treats telemetry gaps as unknown and excludes them from both sides of the comparison
Returns evidence and never a stated cause — the data cannot separate loss from inefficiency
Normalises litres, kWh and runtime hours correctly, with no silent unit conversion
Flags fewer than 20 sites in total, so the output is investigable by a real team

Pass barAt least 12 of 14 found with fewer than 20 total flags, and no output naming a cause. Any response that asserts theft fails the lab regardless of its accuracy.

StretchAdd a second pass ranking the flagged sites by recoverable value, so the investigation team works the list in the order that pays.

Assessment

Assessed, not attended

Every module ends with a timed, randomised assessment delivered through the SCIKIQ Agentic AI Playground. The certificate requires a pass on all of them plus two reviewed capstones.

Assessment blueprint

What each test covers, how long it runs, and how many items are currently in the versioned bank behind it.

#	Assessment & coverage	Items	Time	In bank
01	Operating model & scopingDelivery mandateTowerco economicsValue sizingThin slicing	25	35 min	3
02	Infrastructure data landscapeAsset registersTelemetry and gapsEnergy dataScanned documents	25	35 min	2
03	Context engineeringIdentifier resolutionClause extractionUnitsUncertainty	25	35 min	2
04	Retrieval & groundingClause retrievalAmendment chainsGraph traversalRetrieval metrics	25	40 min	2
05	Agent design for operationsFailure predictionEnergy anomalyDispatch decisioningEscalation	25	40 min	2
06	Systems integrationMCP designWrite-back safetyReport generationIdentity	25	35 min	2
07	Segregation, security & riskTenant isolationConfidentialityLLM securityIncident response	30	45 min	2
08	Production & economicsEval gatingFalse dispatchDrift and thresholdsCost per site	30	45 min	2

Single best answerOne defensible option among plausible distractors drawn from real field mistakes.

Scenario judgementA client situation with constraints; you pick the action a Senior SCDAI Engineer would take.

Architecture selectionGiven non-functional requirements, choose the pattern and justify the trade-off.

Failure diagnosisA trace, an eval report or a cost curve — identify the root cause.

Each module ends with a timed assessment; the program certificate requires a pass on every one.
Items are drawn at random from a versioned bank, so no two learners sit an identical paper.
Pass mark is 70%. Two retakes are included, each with a fresh draw.
Scenario items carry double weight — they are the ones that predict field performance.
Capstones are reviewed against a published rubric by a Senior SCDAI Engineer, not auto-graded.

Try four questions from the bank

Real items, drawn from 17 in this program's bank — weighted toward the scenario and diagnosis types, because those are the ones that predict field performance. Instant feedback, nothing saved.

Four sample items — one attempt each, then the reasoning is shown.

Q1A towerco asks for "AI to reduce cost". Where does the value case almost always start?

Power is the dominant controllable line at site level. Anything that reduces fuel burn or finds losses moves the P&L faster than any other operational intervention here.

Q2The asset register says a site has two tenants; telemetry shows three equipment sets drawing power. What do you do first?

Register-to-reality drift is normal and consequential. Measuring it early sets an honest ceiling; auto-updating a commercial record from telemetry is not yours to do.

Q3A lease has three amendments. What must clause retrieval return for "current rent"?

The original document is almost never operative. Resolving the chain and citing which amendment governs is the whole job.

Q4An energy agent finds a site where fuel delivered exceeds what runtime and load can explain. How should it present that?

Genuine inefficiency, metering error and pilferage all look alike in the data. Presenting evidence rather than an accusation is both fairer and more likely to be acted on.

Career & outcomes

What this makes you, and where it goes next

The same ladder whichever specialisation you enter through — what changes is the domain you go deep in. Below: how the program is delivered, the skills it moves, the roles it leads to, and the specialisations closest to this one.

Available two ways

The same labs, assessments and capstones, delivered to an enterprise cohort or to individual professionals.

B2B

Direct to enterprise

Cohorts of 20 to 2,000+ on your own tenancy, with your data patterns and your cloud. Skill-gap baselining up front, per-team mastery reporting throughout, and capstones scoped against your real backlog so the output is deployable work.

Private cohort on your tenancy
Baseline skills assessment before kick-off
Capstones scoped to your backlog
Per-team mastery dashboards for L&D
Optional Senior SCDAI Engineer architecture reviews

B2C

Direct to individual professionals

The same labs, assessments and capstones for individual engineers and analysts, run on shared infrastructure with a fixed cohort calendar. You leave with a graded portfolio, not a certificate of attendance.

Fixed-date cohorts, self-paced within them
Same graded labs and item bank
Portfolio-ready capstones you own
Certificate on a pass in every module
Community and office-hours access

Skill matrix

Find your row and aim one column right. The Playground scores you against this after every module.

Skill	Beginner	Intermediate	Advanced
Infrastructure domain fluency	Knows what a towerco is.	Maps tenancy, energy and SLA to the P&L.	Sizes energy and penalty value credibly.
Asset & telemetry data	Reads site telemetry.	Reconciles register, telemetry and fuel at site level.	Designs for monitoring gaps without overstating uptime.
Contract intelligence	Extracts a clause.	Resolves operative terms through amendment chains.	Runs population-level extraction with measured clause accuracy.
Context engineering	Writes clear prompts.	Structures retrieval, tools and state deliberately.	Designs context strategy for reliability and cost at scale.
Retrieval & grounding	Builds basic vector search.	Tunes chunking, hybrid search and reranking.	Designs graph + vector grounding with measured recall.
Agent orchestration	Runs a single tool-calling agent.	Builds supervised multi-step and multi-agent flows.	Designs autonomy boundaries and failure containment.
Tool & system integration	Calls a documented API.	Writes an MCP server over a system of record.	Designs a least-privilege tool estate across systems.
Evaluation	Eyeballs outputs.	Builds labelled eval sets and regression gates.	Runs online evals with drift and judge calibration.
Observability & cost	Reads logs.	Traces runs, tracks tokens and latency.	Owns cost per task and capacity planning in production.
Security & guardrails	Adds output filters.	Mitigates the OWASP LLM Top 10 in a build.	Threat-models an agent estate and proves controls.
Client delivery	Takes notes in a workshop.	Runs discovery and scopes a thin slice.	Owns the account technically, from scope to handover.

Where it takes you

The SCDAI ladder is the same whichever specialisation you enter through — what changes is the domain you go deep in.

Data and AI EngineerEntry

Python, LLM APIs, first RAG build, prompt and context basics.

Associate Data and AI EngineerPractitioner

Client-facing discovery, tool integration, agent build under supervision.

SCIKIQ Certified Data and AI EngineerSpecialist

Owns a deployment end to end — scoping, build, evals, handover.

Senior Data and AI Engineer / Domain LeadSenior

Multi-agent architecture, regulated deployments, reference patterns for the practice.

AI Architect / Practice LeadLeadership

Portfolio of accounts, delivery standards, hiring and capability strategy.

Terms worth knowing

Tenancy ratio: Average number of tenants per tower — the core towerco value driver.

Co-location: Adding a further operator’s equipment to an existing structure.

Ground lease: The agreement with the landowner, and the towerco’s main long-term obligation.

RMS: Remote Monitoring System — site telemetry for power, access and alarms.

Excludable outage: Downtime the tenant agreement excludes from SLA calculation.

Passive infrastructure: The tower, power and shelter — everything except the active radio equipment.

Escalation clause: The contractual rent increase mechanism in a ground lease.

Pilferage: Fuel removed from a site by means other than consumption.

SCDAI: SCIKIQ Certified Data and AI Engineer — the credential this program awards.

SCDAI engineer: An engineer embedded with the customer who turns their constraints into a working system.

Thin slice: The narrowest end-to-end path through a workflow that still produces business value.

Agentic RAG: Retrieval where the agent decides when, what and how often to retrieve.

MCP: Model Context Protocol — open standard connecting an agent to tools and data.

A2A: Agent2Agent — protocol for agent-to-agent delegation across vendors.

Eval set: A labelled, versioned set of cases an agent must pass before release.

LLM-as-judge: Using a model to score outputs, calibrated periodically against human ratings.

Guardrail: An enforced constraint on an input, output or action — not a prompt request.

Human-in-the-loop: A required human approval step before a consequential action executes.

Cost per task: Total token, tool and compute cost of one completed unit of work — the engineer's unit economic.

Golden path: The reference architecture a practice standardises on so deployments stay reviewable.

Handover: The point at which the client can operate, evaluate and change the system without the engineer.

Related programs

Skill a team, or join a cohort

B2B cohorts run on your tenancy with capstones scoped to your backlog. B2C cohorts run on a fixed calendar.

Talk to us

Enrolling

What you need, what you get, and what it costs

Stated plainly enough to rule yourself in or out without a sales call: the prerequisites, how the program runs, exactly what the credential is worth, and the questions everyone asks.

Before you start

Stated plainly so you can rule yourself in or out without a sales call. Nothing here is a formal qualification — it is what the first lab assumes you can already do.

Technical

You should already be able to do these

Comfortable in Python: functions, typing, virtual environments, reading a stack trace
Have called an LLM API and handled a structured response
Git, the command line, and reading someone else’s code without panic

Domain

What we assume, and what we teach

No infrastructure background needed, but be ready to treat a contract as data
Comfort with time-series telemetry and with the fact that remote sites go dark

Time and access

What the program asks of your week

A machine that can run a browser and a terminal — labs run in the Playground, not locally
Roughly 8 hours a week for the taught weeks, plus 20 hours for the two capstones

How it runs

Cohort dates and pricing are confirmed on enquiry rather than printed here, because both move with the intake.

Format: Self-paced within a fixed cohort window, delivered in the SCIKIQ Agentic AI Playground
Taught hours: 80 hours across 8 modules
Capstones: 20 hours — two end-to-end builds, reviewed not auto-graded
Typical duration: 10–12 weeks at around 8 hours a week
Level: Intermediate → Advanced
Built for: Engineers and analysts deploying AI across passive telecom infrastructure
Next cohorts: — confirmed on enquiry
Pricing: — confirmed on enquiry

The credential

The credential is awarded per specialisation, so it names the domain or stack you were assessed in rather than claiming general competence. On this program the badge reads SCDAI — Telecom Towers.

How it is earned

A pass at 70% or above on every module assessment — not an average across them
Both capstones reviewed against the published rubric by a Senior SCDAI Engineer
All labs submitted and machine-graded green

Awarded as: A verifiable digital badge with a public credential record
Valid for: Two years from award
Recertification: One current-year assessment plus a refreshed capstone
Retakes: Two included per assessment, each with a fresh item draw
Named specialisation: The badge states the domain or stack, e.g. SCDAI — Healthcare
Verification: Third parties confirm a holder from the credential record, no login required

A certificate that cannot be checked is decoration. Every award resolves to a record showing the specialisation, the award date and the assessments passed.

The certificate you leave with

This is the credential the program awards, shown exactly as it is issued — with the specialisation named, the assessment record attached and a verification link anyone can check without an account.

SCIKIQ Data Academy Certificate of Professional Certification

This is to certify that

Your name

has been assessed and certified as

SCIKIQ Certified Data and AI Engineer

Telecom Towers

Specialisation: Telecom Towers
Assessments passed: 8 of 8, each at 70% or above
Labs graded: 40 of 40 machine-graded green
Capstones reviewed: 2, against the published rubric
Taught hours: 80 across 8 modules, plus 20 capstone hours

AwardedOn completion

Valid untilTwo years from award

Credential IDSCDAI-TELECOM-TOWE-<issued>

Senior SCDAI Engineer, Certification Board

01
The specialisation is on the certificate
Not “Data and AI Engineer” but the domain or stack you were actually assessed in. A general claim would be a weaker one.
02
The assessment record travels with it
Modules passed, labs graded and both capstones reviewed — so the credential states what was measured rather than that you attended.
03
Anyone can verify it, without a login
The credential ID resolves to a public record showing the specialisation, the award date and the assessments passed.

A certificate that cannot be checked is decoration. Every award resolves to a record showing the specialisation, the award date and the assessments passed.

Questions we get asked

The objections that come up in every conversation about this program, answered without the brochure voice.

Is this a course or a certification?

Both, and the second is the point. Eight timed assessments and two reviewed capstones stand between you and the credential, so a pass means someone measured the skill rather than recorded your attendance.

How is it self-paced without falling apart?

Every lab is provisioned, graded and unblocked by the Playground rather than by an instructor. That is what lets a cohort of 2,000 cost the same faculty time as a cohort of 20 — and why you are never waiting on someone to mark your work.

What if I fail an assessment?

Two retakes are included per assessment, each drawing a fresh item set from the bank, so a retake is a genuinely new paper rather than the same questions again.

Do I need my own cloud account?

For the domain specialisations, no — labs run in provisioned sandboxes. For the four tech-stack programs you will want access to that platform, since deploying into a real subscription is much of the point.

Can we run this privately for our team?

Yes. B2B cohorts run on your own tenancy with your data patterns, a skills baseline before kick-off, per-team mastery reporting, and capstones scoped against your actual backlog so the output is deployable work rather than an exercise.

Which specialisation should I take?

Take the domain you deploy into. If you move across industries, take a tech-stack program instead and pick up domain context on the engagement. The chooser on the programs page will narrow it.

How current is the material?

The trends, platform capabilities and regulatory positions are reviewed each quarter, and every external claim on these pages links to its source so you can check the date yourself.

What do I actually leave with?

A graded portfolio: forty machine-graded labs, two reviewed end-to-end agent builds with measured evaluation and cost per task, and a verifiable credential naming your specialisation.

Still deciding?

Tell us the systems you deploy into and we will say plainly whether this specialisation is the right one — or which of the 21 is.

Talk to us

SCIKIQ Certified Data and AI EngineerTelecom Towers

The value chain, the people, the trends

The tower value chain

Acquire & permit

Decisions made here

Where it breaks today

What is measured

Lease up & co-locate

Decisions made here

Where it breaks today

What is measured

Operate & maintain

Decisions made here

Where it breaks today

What is measured

Power & energy

Decisions made here

Where it breaks today

What is measured

Assure SLA & bill

Decisions made here

Where it breaks today

What is measured

Portfolio & investment

Decisions made here

Where it breaks today

What is measured

Who you are building for

What the sector is seeing

Energy is the value pool

Tenancy ratio drives everything

Remote monitoring finally covers the estate

SLA disputes are an evidence problem

Lease portfolios are the hidden liability

Renewables shifted from ESG to economics

Domains, ontology, taxonomy and governed terms

The data landscape

Site & asset register

Remote monitoring

Energy

Leases & permits

Work orders & field

SLA & billing

The semantic layer

Data domains

Site & structure

Tenancy

Land & permits

Energy

Operations

Service level

Ontology & knowledge graph

Site

Structure

Landlord

Ground lease

Permit

Tenant

Tenancy

SLA

Power source

Fuel delivery

Outage

Work order

Taxonomies

Asset hierarchy

Outage cause

Energy source

Governed business terms

How it is actually built

The reference architecture

Sites, telemetry & contracts

Ingestion & site context

Site model & semantics

Retrieval & grounding

Agent & orchestration

Tenant segregation & guardrails

Evaluation & observability

Typical tech stack

Models