AI Ops Engineer Corporate Training 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
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Enterprise learning solutions built for corporate teams.
Go beyond standard classroom delivery with enterprise-ready learning infrastructure, managed execution, capability insights, and production-like practice environments designed for corporate scale.
Enterprise Command Center (LMS+)
Managed Batches (End-to-End Execution)
Capability Audits (Pre-Training Intel)
Custom Chaos Sandboxes
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.
Production-first SRE discipline
This AI SRE training programme moves engineers from classical ops into LLM-specific failure modes, observability stacks, and reliability engineering for live AI services.
End-to-end eval pipelines
Learners build offline and online evaluation pipelines RAGAS, TruLens, promptfoo, DeepEval integrated into CI/CD with regression gates on faithfulness and relevance.
AI incident management training built in
Every cohort runs game-day drills, writes production-grade runbooks, and practises structured post-mortems for the 6 most common AI outage patterns.
FinOps for LLMs training at scale
Engineers learn cost-per-feature dashboards, quota enforcement, model routing strategies, and chargeback systems the conversation that proves AI investment pays back.
Drift and resilience coverage
Data drift, embedding drift, prompt drift, chaos injection, and multi-region resilience the surveillance and fault-tolerance discipline unique to AI in production.
Capstone evaluated by industry SREs
Each engineer presents a full ops stack monitoring, evals, alerts, runbooks, cost dashboard, and red-team findings to a joint NovelVista and industry SRE evaluation panel.
A four-milestone path from skill gap to client-ready.
AI failure modes and observability foundation
Establish fluency in how AI systems fail differently from classical services; instrument Prometheus, Grafana, and OpenTelemetry across the full AI service stack.
Evaluation, drift, and SLO engineering
Build offline and online eval pipelines; detect data, embedding, and prompt drift; define SLIs, SLOs, and error budgets the business accepts.
Resilience, FinOps, and security operations
Run chaos and resilience testing on AI services; implement FinOps for LLMs training disciplines cost dashboards, quota systems, model routing, and quantisation; and execute a red-team exercise.
Capstone and credential
Engineer designs and operates a full AI ops stack on a supplied live service evaluated jointly by NovelVista AI practice and an invited industry SRE leader.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Trusted by Industry Leaders for Enterprise AI Upskilling
See why CEOs, CTOs, and business leaders collaborate with NovelVista
to discuss the future of AI, digital transformation, and workforce readiness.
- Exclusive AI leadership summits featuring enterprise decision-makers and technology experts
- Recognized corporate training partner for AI, Agile, DevOps, ITSM, and cybersecurity programs
- Trusted by organizations to build future-ready teams with practical, industry-focused learning
- Real conversations, real business challenges, and actionable AI transformation insights from industry leaders
Learn from domain experts with 15+ years of experience.
"My job is not to teach monitoring tools it is to build engineers who can own the full reliability, cost, and security surface of a live AI service the moment they walk back into their team."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·SREs, DevOps and platform engineers, and senior production-support engineers (4+ years) who own the reliability and economics of live AI services
- ·Engineering teams enrolling in AIOps engineer corporate training to close the gap between classical DevOps competency and LLM-specific operations
- ·Platform and infrastructure engineers responsible for deploying, monitoring, and cost-governing LLM-based applications in production
- ·Reliability leads at services firms and BFSI accounts where AI services must meet formal SLOs and compliance requirements
- ·L&D leaders building an AI SRE training programme for engineering cohorts transitioning from classical ops to GenAI production environments
Pre-requisites
- ·Strong hands-on background in Docker, Kubernetes, and CI/CD this programme extends those skills into the AI domain
- ·Working familiarity with Prometheus, Grafana, or ELK learners should be comfortable with classical observability tooling before joining
- ·No prior LLM or machine learning background required AI concepts are introduced progressively through an SRE lens
- ·Enterprise cohorts should align on lab environment access (cloud or on-prem Kubernetes clusters) before the programme kick-off
Trusted by L&D leaders across the world.
"The AIOps engineer corporate training gave our SRE team a complete ops playbook for LLM services from observability and evals to FinOps and runbooks. We deployed the cost dashboard within two weeks of the programme closing."
"The AI incident management training was the most practically useful section. Our on-call team ran the game-day drill and immediately identified gaps in our alerting and runbook coverage that we closed before the next production release."
"FinOps for LLMs training was something we had been looking for across multiple vendors. The cost-per-feature dashboard and model routing lab alone justified the programme investment for our account."
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.