AI hiring is changing.
Get ready before it's obvious to everyone.
GCCs are turning from services centers into product companies — and interviewing very differently for it. Our live seminars are working sessions on exactly this shift: what the new AI interviews really test, and how to position yourself ahead of the curve, in India and abroad.
Want the full framework? The 4-week Applied AI course takes you end to end.
What you walk away with
Every seminar is priced to cover the infrastructure of a hands-on session — and to keep the room serious. In exchange, you leave with material you'll actually use.
Curated tools list
The exact tools used day-to-day in applied AI delivery work — not a directory dump, a working shortlist.
Forward Deployed guide
The playbook for operating as the person who takes a business problem, sits inside the domain, and ships.
Multi-cloud stack comparison
How the AI stacks compare across clouds — what to reach for, what to avoid, and what interviewers expect you to know.
Hands-on product walkthroughs
Live inside Forward Deployed Studio, AI DLC, EvalForge, and IDP — the finished work first, then how it's built.
The ADLC Framework
A 6-phase lifecycle — Discovery → Framing → Architecture → Build → Eval → Production — mapped to every non-engineering role. You leave knowing which phase you own and what 'done' looks like in each one.
Spec Owner Pattern
The evolution of Product Owner for AI. Instead of user stories, you write AI Specs: eval thresholds, guardrail boundaries, handoff conditions, and fallback behavior. We write one in the session.
Eval Without Code
What evaluation means for probabilistic AI systems — and why QA, BAs, and PMs own more of it than they realize. Includes a live EvalForge walkthrough on a real prior authorization use case.
4-week curriculum
Each week is designed so that every role walks away with something concrete — not just awareness, but a specific skill or deliverable they can use at work.
- ▸The 6 phases: Discovery → Framing → Architecture → Build → Eval → Production
- ▸Where your role fits in each phase — breakout by PM / BA / QA / Engineer / Ops
- ▸How to read an AI architecture doc without being an ML engineer
- ▸The language translation: what ML teams say vs what business people hear
Maps exactly which ADLC phases they own — framing, stakeholder reviews, go/no-go — and what 'done' looks like in each one.
Knows which phase each artifact belongs to — intake interview, edge-case catalog, data dictionary — and when to hand off to engineering.
Sees eval as a first-class ADLC phase, not an afterthought — and understands that defining eval boundaries is their deliverable, not a dev task.
Understands how to sequence an AI sprint when ADLC phases overlap across teams and how to manage cross-team dependencies without an ML background.
Learns to communicate phase progress in language PMs and business stakeholders understand — reducing back-and-forth on scope and acceptance criteria.
- ▸The shift from Product Owner (user stories) to Spec Owner (AI Specs)
- ▸What an AI Spec contains: eval thresholds, guardrail definitions, human-handoff rules, scope boundaries
- ▸Live workshop: write a real AI Spec in the Studio for a use case in your industry
- ▸How to scope a POC and define the go/no-go milestone without ML expertise
Writes a complete AI Spec — obligation, guardrail, handoff trigger, eval threshold, fallback — for a real use case from their current role.
Runs a structured AI intake interview to surface the data, edge cases, and scope boundaries that the AI Spec needs to be accurate.
Defines the eval boundaries in the spec — exactly what pass looks like, what score triggers human review, and what constitutes a failed output.
Sets the POC milestone decision criteria: which metrics define go/no-go before committing to a full build, and who owns the sign-off.
Can read, challenge, and implement against an AI Spec without writing it — and knows how to push back when a spec is ambiguous or under-defined.
- ▸What 'eval' means and why BAs and QA own more of it than they think
- ▸LLM-as-judge vs human eval — when to trust each, how to design both
- ▸Red-teaming for non-engineers: finding failure modes without writing code
- ▸Observability basics: which metrics PMs and BAs should review in production
Knows exactly which metrics to review in production stand-ups — pass rate, red-flag count, drift rate — without needing to read code or ML dashboards.
Designs a human eval rubric for the use case: scoring criteria, escalation conditions, and how to spot when the model is drifting from business intent.
Builds a complete red-team scenario set — edge inputs, adversarial prompts, and fallback verification steps — that can run on a schedule without dev involvement.
Defines the observability SLA: which metrics trigger alerts, what latency thresholds are acceptable, and who gets paged when the eval score drops.
Knows what to instrument from day one and how to wire EvalForge metrics to production observability so the team can monitor quality without manual checks.
- ▸The go/no-go checklist a non-engineer can run independently
- ▸Rollback triggers, incident vocabulary, and escalation paths for AI systems
- ▸How to lead an AI review meeting — questions to ask, red flags to surface
- ▸Final presentation: AI Spec + Eval Plan + Production Checklist for your use case
Can lead a production readiness review independently — asking the right questions, surfacing red flags, and signing off without waiting for an engineering briefing.
Creates the change-request process for AI system updates — how spec changes flow back through the lifecycle when business rules or data changes.
Owns the regression suite for AI output drift: scheduled red-team re-runs, rollback criteria, and a documented handoff process when incidents occur.
Runs the final go/no-go meeting, documents the escalation path for incidents, and holds the team accountable to the production checklist.
Presents the full portfolio — ADLC map, AI Spec, eval plan, production checklist — to cross-functional stakeholders as a structured delivery narrative.
Live Cohort — waitlist open · Self-Paced available now · Regional pricing for IN, US, CA, UK
Get notified when a seminar opens.
Seminars are capped to keep them hands-on, and seats go fast. Drop your email and we'll tell you the moment the next session opens for registration.
Questions
What happens in a live seminar?
They're working sessions, not webinars. Each seminar covers how AI hiring and delivery are actually changing — right now, the shift in how GCCs hire for AI roles as they move from services to product — with hands-on walkthroughs of Forward Deployed Studio, AI DLC, EvalForge, and IDP. You leave with the curated tools list, the Forward Deployed guide, and the multi-cloud stack comparison.
Why are seminars paid?
The ticket price (₹350 for India sessions) mainly covers the infrastructure and tooling costs of running a hands-on session — live tool environments aren't free to operate. It also keeps the room filled with people who are serious about landing these roles, not passersby collecting links.
Are the seminars a preview of the course?
Partly. Seminars go deep on one live topic and give you real deliverables on their own. If the way of working clicks, the 4-week course takes you end to end — ADLC, Spec Owner, eval frameworks, production readiness — on your own use case.
Who is the course for?
PMs, BAs, QA engineers, delivery leads, and software engineers who work alongside AI — or will soon. You don't need an ML background. You need to be in a role where AI is entering your workflow and you want a framework for working with it confidently.
What's the difference between Self-Paced and Live Cohort?
Self-Paced gives you lifetime access to course content to work through anytime, plus 1 month of free tools. Live Cohort adds weekly instructor-led sessions, group workshops (AI Spec writing, eval rubrics), and a Slack community, plus 3 months of free tools. Both cover the same 4-week curriculum.
What is the 'Spec Owner' pattern?
Spec Owner is the natural evolution of Product Owner when features are AI. Instead of writing user stories, you write AI Specs — documents that define eval thresholds, guardrail requirements, human-handoff rules, and what the model is and isn't responsible for. Week 2 teaches this with a live workshop. You'll write one before the session ends.
What happens to my tools access after the free window?
After your free window (1 or 3 months), you pay on-demand per session — $9 for a Studio session, $15 for a practice mock, $19 for an AI Alignment Pack. Or subscribe at $69/mo for unlimited access to everything.
How large is a cohort?
We cap Live Cohort at 20 participants to keep workshops high-signal. Each cohort includes 2 live guest practitioner sessions — external AI practitioners from enterprise deployments — capped at 2 per cohort. Once a cohort fills, the next one opens the following month.
Can I upgrade from Self-Paced to Live Cohort?
Yes. Pay the $400 difference before the next cohort start date and we'll move you into the live track.
Can my team enroll together?
Yes. Teams of 3 or more from the same organization get custom cohort pricing and we can align the course use cases to your actual AI projects. Contact us at support@forwarddeployed.app for team pricing.
Position yourself before the market catches on.
Start with a live working seminar. Go deeper with the 4-week course — Self-Paced $499 · Live Cohort $899. Tools free during your course, on-demand after.