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Course

AI Evals Career Fast Lane: Become the AI Trust Operator (4 Weeks)

Srini Annamaraju

Enterprise AI Trust & Evals Leader

Shen Pandi

AI Strategist & Evaluations Architect

AI’s New Career Edge: Proving Models Are Safe To Use. With Evals.

Enterprise AI is landing in real workflows fast. Yet, most teams still can’t answer basics: what does “good” mean, what’s unacceptable, who decides, and how do we know it stays good after go-live?

That gap is where AI Evals sits. Shen and I are in the middle of this shift: CIO/CDO/CAIO teams are asking us to define Evals functions, write job specs, and build “acceptable behaviour” rubrics. Demand is getting hot.

Evals = a repeatable way to set expectations, define red lines, spot failure modes, and keep AI accountable in production (beyond accuracy).

This course is career-led and practical. For ~2–3 days of your bill rate or salary, you’ll become Evals-career ready and lead the conversations that decide whether AI ships, pauses, or gets killed.

Across 4 live sessions we map you to our E/D/S/O lanes: Echo sets the trust bar, Delta builds evals, Sigma runs monitoring + drift playbooks, Omega defends decisions with execs/boards/regulators.

You’ll leave with real artefacts: an Evals Brief, red lines, an adoption rhythm, plus career assets (Positioning Sheet, Adoption Plan, Story Pack) and a 6-month plan you can run with Product, Engineering, Risk, and leadership.

What you’ll learn

Turn enterprise AI evals into your career engine: design trust systems, prove model value, and become who leaders trust when AI gets real.

  • Create an Evals Career Positioning Sheet (target lane, gaps, proof signals, and a 6-month plan you can actually execute).

  • Identify the 5 proof signals hiring managers look for in Evals-adjacent roles (and translate your past work into those signals).

  • Build a 6-month execution plan with weekly actions (portfolio proof, internal scope moves, networking targets, and interview readiness miles

  • Draft a reusable Evals Brief (what “good” means, red lines, key risks, and the decision questions execs actually care about).

  • Turn a vague AI idea into clear expectations by defining what “good” and “bad” actually mean for a real use case (in business English)

  • Ask the right questions of technical teams so you can sanity-check an AI rollout without pretending you’re an eval engineer.

  • Build an Adoption Plan (stakeholder map, talking points, escalation paths, and a simple operating rhythm to keep it alive).

  • Create a lightweight operating rhythm (what gets reviewed wkly/mthly, who signs off, what triggers escalation) that doesn’t die after week 2

  • Handle the awkward politics when AI performance drifts or incidents happen, using simple scripts that protect users and protect your career.

  • Build a Gig Kit (your niche, 2 packaged offers, indicative pricing, proof assets, plus a first outreach message you can send).

  • Gain confidence with real experience (industry + use case + risk profile + tools) so you don’t look like another generic AI consultant.

  • Write your first outreach + proof assets (a short pitch, a one-page capability sheet, 2–3 proof bullets) so you're taken seriously.

Learn directly from Srini & Shen

Srini Annamaraju

Srini Annamaraju

Contact

Founder; Client-side enterprise AI + Evals work at London-based IntelStack

Previously at
Capgemini
HPE
Tata Consultancy Services
Shen Pandi

Shen Pandi

Contact

Founder; builds and ships AI + evals programs used in real enterprise deployment

Previously at
McKinsey & Company
EY
Deloitte
Kearney
See all products from School of Enterprise AI Evals

Who this course is for

  • Mid-career QA/BA/PMs etc. fearing replacement by AI-native folks, eyeing promotions or a pay jump into AI trust, as evals hiring ramps.

  • Risk, Compliance or Ops leads suddenly owning GenAI risk, needing plain-English trust artefacts as scrutiny tightens.

  • Consultants/architects sharpening their personal offer (job or side gig), using evals literacy as demand shifts.

Prerequisites

  • 6+ months in a product/ops/QA/risk role

    Needed so you can map evals ideas and your use cases to real workflows, stakeholders, and decision-making.

  • Comfort writing clear docs (1-pagers, tickets, PRDs)

    You’ll produce briefs, red lines, and adoption plans; sloppy writing kills the value.

  • Basic AI literacy (what LLMs are, prompts, hallucinations)

    We won’t teach GenAI 101; you need enough baseline to follow evals discussions.

What's included

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