AI Ops Engineer — Reliability, Performance & Cost for Production AI
capability building,
designed for your organisation.
A custom-built corporate programme for SREs, DevOps and platform engineers, and senior production-support engineers (4+ years) owning the reliability and economics of live AI services. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Five real AI outages — RCA deep-dive: token overrun, retrieval failure, model regression, prompt injection, supply-chain compromise
- Why AI systems fail differently from classical services
- The 2026 incident-management playbook for AI
- Lab: tabletop incident on a simulated AI outage; produce a structured RCA
Want the full module-by-module syllabus, sample assignments, and pricing?
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Demonstrable skills your team will apply on live projects.
Run production AI services with proper observability
Logs, metrics, traces, online evals, drift detection — all integrated into your existing observability stack (Prometheus, Grafana, OpenTelemetry, Langfuse, Arize).
Own incident response for AI outages
Runbooks for 6 most common AI failure modes; game-day incident drills; rigorous post-mortem discipline.
Cut AI service cost by 40-60% via FinOps and routing
Caching, semantic cache, model routing, quantisation, autoscaling, GPU/CPU mix optimisation.
Pass the joint capstone evaluation panel
Each engineer designs and operates a full ops stack on a supplied AI service — with monitoring, evals, alerts, runbooks, cost dashboard, red-team report, and a post-mortem from a scripted game-day.
Earn the AI Ops Engineer credential
Cohort first-attempt completion rate of 92%. Two attempts permitted. Designed for SRE-leveraged services-firm and BFSI accounts.
Lead AI reliability at scale
AI Ops engineers are the rarest profile in 2026 enterprise AI. Alumni typically take a 1-2 grade leap on reliability-critical accounts.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Strong DevOps/SRE background — Docker, Kubernetes, CI/CD — but new to LLM-specific failure modes
- ·Comfortable with Prometheus/Grafana/ELK but haven't instrumented LLM services or evals
- ·No fluency with LLM evaluation frameworks (RAGAS, TruLens, promptfoo, DeepEval)
- ·Cannot independently detect or alert on AI-specific drift (data, embedding, prompt)
- ·Limited experience with FinOps for LLMs — cost spikes, token economics, model routing
- ·No structured incident-management muscle for AI service outages or model regressions
Where they'll arrive
- ✓Incident & service management for AI — runbooks, alerts, on-call, post-mortems for LLM services
- ✓Continuous monitoring — logs, metrics, traces, and online evals across the AI stack
- ✓AI operations management — owns day-2 ops for AI apps across environments
- ✓Production reliability engineering for AI — SLIs/SLOs/chaos and resilience practices
- ✓AI performance optimisation — caching, routing, batching, quantisation discipline
- ✓Security & compliance ops — prompt injection, audit trails, data residency, responsible AI in production
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
DevOps focuses on software delivery, MLOps manages ML model lifecycles, and AI Ops extends operations to production GenAI systems, LLM observability, reliability, evaluation, and cost optimization.