LinkedIn profile & professional branding
Position yourself as an AI-native engineer rather than a tester who took a course. Headline, about section, project write-ups and activity strategy.
Career outcomes
Where 100 days can take your career
Titles vary by employer, but the underlying capability is consistent: you can design an AI system, evaluate it honestly, operate it under cost and latency constraints, and explain every decision you made.
Target roles
Ten roles this program prepares you for
Some are a lateral move that makes you far harder to replace. Others are a genuine change of discipline. Your mentor helps you pick a realistic target in the career workshops.
| Role | What the job actually involves |
|---|---|
| AI Test Engineer | Applies AI to test design, generation and maintenance inside an existing quality function. |
| AI Quality Engineer | Owns quality for AI systems themselves — evaluation datasets, hallucination checks, guardrails. |
| AI Engineer | Builds LLM applications end to end: retrieval, tools, orchestration, deployment. |
| GenAI Engineer | Focuses on generative applications, prompt and context engineering, and model selection. |
| Agentic AI Engineer | Designs multi-agent architectures with planners, executors, critics and human approval. |
| RAG Engineer | Specialises in ingestion, chunking, hybrid retrieval, reranking and grounded answering. |
| LLM Engineer | Works close to the model: structured output, tool calling, token and cost optimisation. |
| AI Automation Architect | Sets the architecture and standards for AI-assisted automation across teams. |
| AI Platform Engineer | Runs the platform: observability, cost control, CI/CD and rollback for AI services. |
| AI Solutions Architect | Translates business problems into defensible AI system designs and trade-offs. |
Career sessions & support
Preparation is a module, not an afterthought
Resume optimisation for AI-native roles
Rewrite your experience around systems, decisions and measurable outcomes — the vocabulary AI hiring panels screen for.
Mock interviews
Domain-specific and agentic AI panels, with structured feedback on how you reason out loud and handle follow-up pressure.
One-on-one mentorship
Personal doubt-clearing throughout the program and during your active job search.
Job application & interview strategy
Which roles to target, how to sequence applications, and how to talk about a career transition without apologising for it.
Community & industry use cases
Ongoing access to peers and to real-world implementation patterns from enterprise delivery.
Certificate of completion
Supporting evidence for your application — the portfolio does the heavy lifting.
Support period
Revisit classes and assignments as needed, and get guidance until preparation converts into offers.
The honest version
What actually gets you hired
Not the certificate. Not the tool list on your resume. In every AI engineering panel we have prepared people for, the deciding moment is the same: you are asked to explain a system you built, and then pushed on why.
Why chunk at that size. Why an agent instead of a chain. Why that locator strategy. What happens when retrieval returns nothing relevant. What it costs per run, and what you would cut first if the budget halved.
Candidates who have only followed tutorials stall at the second follow-up question. Candidates who have built, broken and reviewed eight systems do not. That is the entire design principle behind this program.
Questions
Career questions, answered honestly
We deliberately do not publish salary promises. Compensation for AI-native roles varies enormously by city, employer type, years of experience and domain — a fintech in Bangalore, a GCC in Hyderabad and a product company in the Bay Area price the same skill very differently. Our career sessions cover how to research and negotiate your own range using live listings rather than marketing figures.
No. We provide placement assistance: resume optimisation for AI-native roles, LinkedIn positioning, mock interviews, job-application and interview strategy, and guidance during your active search. Any training provider guaranteeing employment should be asked to put that in a contract.
Increasingly, yes — in AI engineering roles specifically. Teams hiring for RAG, agents and evaluation want people who can ship and reason, and domain depth in testing or delivery is an advantage rather than a handicap. The portfolio is what gets you past the screen.
That is the most common path. Many learners are not trying to leave quality engineering — they are trying to be the person in their organisation who leads its AI adoption. That is a promotion track in itself.
Next cohort
Target the role, then build for it.
Talk to admissions about which track and which capstone brief best match the roles you want.
Cohort snapshot35 seats
01100 days live + self-paced15 wks
02Mentor-led weekend classes10 hrs/wk
038 guided projects + capstoneportfolio
04Interview prep & career supportongoing
Weeknight office hours · lifetime access to recordings · one accountable mentor across all 100 days