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
Founder; Client-side enterprise AI + Evals work at London-based IntelStack
Shen Pandi
Founder; builds and ships AI + evals programs used in real enterprise deployment
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
Live sessions
Learn directly from Srini Annamaraju & Shen Pandi in a real-time, interactive format.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
4 live sessions • 14 lessons
Week 1
Feb
19
Session 1 (Live): Pick your Evals lane and make a 6-month move
Item A (required): After Session 1: Your Career Positioning Sheet (v1)
Item B (optional): Field task (20 mins): Proof-signal scan
Office Hours (Async): Positioning Sheet feedback
Reflect: Wrap-up
Week 2
Feb
26
Session 2 (Live): Lead the room with a shared “trust” language
Item A (required): After Session 2: Draft your Evals Brief (v1)
Item B (optional): Field task (20 mins): 15-minute “trust check” chat
Office Hours (Async): Evals Brief feedback
Schedule
Live sessions
2 hrs / week
Four live sessions (90 mins each) focused on hands-on workshopping. Come with your use case, ask questions, and leave each week with a clear “ship this next” deliverable. Recordings are available, but live is where you get unstuck fastest.
Thu, Feb 19
4:00 PM—5:30 PM (UTC)
Thu, Feb 26
4:00 PM—5:30 PM (UTC)
Thu, Mar 5
4:00 PM—5:30 PM (UTC)
Thu, Mar 12
5:00 PM—6:30 PM (UTC)
Projects
2 hrs / week
Weekly “ship” milestones to build your AI Trust Pack: Positioning Sheet, Evals Brief, Adoption Plan, and Story Pack (optional Gig Kit). Aim for rough v1s, post them, and iterate using instructor + peer feedback.
Async content
1 hr / week
Lightweight support between live sessions: short checklists, examples, and prompts that help you complete the week’s deliverable. No long video library. The goal is clarity and momentum, not extra homework.
Frequently asked questions
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