AI Forward Deployed Engineering: From Customer Brief to Deployment

Dr. Rajat Dandekar

Vizuara Co-founder | Purdue PhD

Dr. Raj Dandekar

MIT PhD | Vizuara Co-founder

+ Dr. Sreedath Panat

Turn an ambiguous customer request into a deployed, accepted AI solution.

AI demos are easy to show. Forward deployed engineers must make them work with customer data, existing systems and measurable acceptance criteria.

Build one enterprise support-operations pilot: a Python LLM baseline, grounded RAG over a knowledge base, a LangGraph tool-using workflow, and an API-backed application with evaluation, tracing and human approval.

Work with Python, Pydantic, FastAPI, PostgreSQL/pgvector, LangGraph, MCP and Docker. Compare quality, latency and cost; test prompt injection and permission boundaries; prepare rollout, rollback and customer handover.

Leave with a runnable repository, architecture decisions, an evaluation report and a customer-facing case study. Practice discovery, system-design trade-offs, debugging and portfolio defense.

Learn with Vizuara co-founders Dr. Rajat Dandekar (Purdue PhD), Dr. Raj Dandekar and Dr. Sreedath Panat (MIT PhDs).

November 14–December 25, 2026. Saturdays, 10 AM–12:30 PM IST; six live workshops through December 19. Final submissions close December 25. For engineers comfortable with Python, APIs, SQL and Git. This is a simulated customer engagement.

What you’ll learn

Lead an AI delivery engagement from discovery to deployment, customer acceptance and operational handover.

Run discovery interviews and map stakeholders, constraints and the existing workflow.
Translate business goals into a bounded proposal with explicit exclusions.
Write measurable acceptance criteria and an architecture decision record.

Build Python/Pydantic baselines and RAG with pgvector, hybrid retrieval, reranking and cited answers.
Use LangGraph, typed tools and MCP with explicit state, retries, permissions and human approval.
Ship a FastAPI application with Docker, integration tests, evaluation gates, tracing and rollback.

Demonstrate the pilot against agreed tests and explain limitations honestly.
Estimate business value with transparent assumptions and baseline comparisons.
Deliver an operating runbook, rollback plan and ownership handover.

Learn directly from expert instructors

Dr. Rajat Dandekar

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Vizuara Co-founder | Purdue PhD | Teaching engineers to build AI systems

Education & research

Dr. Raj Dandekar

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Vizuara Co-founder | MIT PhD | Practical AI and language-model engineering

Education, research & tools

Dr. Sreedath Panat

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Vizuara Co-founder | MIT PhD | Computer vision & scientific machine learning

Education & research

See all products from Rajat

Who this course is for

Software and AI engineers who can build applications and want to own customer discovery, delivery and production handover.
Solutions engineers and technical consultants moving into hands-on AI delivery, with working Python, API and Git skills.

What's included

Live sessions

Learn directly from your instructors in a real-time, interactive format.

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

A complete customer-delivery case study

Build a portfolio package spanning discovery, architecture, a working pilot, acceptance evidence, business value and operational handover.

Stakeholder and change-request practice

Practice discovery interviews, milestone demos, scope negotiation and a final acceptance review through one simulated engagement.

Maven Guarantee

Your purchase is backed by the Maven Guarantee.

Course syllabus

6 live sessions • 12 lessons

Week 1

Nov 14—Nov 15

AI discovery, model selection and a working LLM baseline

2 items

Nov

Session 1

Sat 11/144:30 AM—7:00 AM (UTC)

Week 2

Nov 16—Nov 22

Enterprise RAG: data pipelines, retrieval and grounded answers

2 items

Nov

Session 2

Sat 11/214:30 AM—7:00 AM (UTC)

Free resources

Schedule

Live sessions

15 hrs

Six Saturday workshops, 10:00 AM–12:30 PM IST: November 14, 21, 28 and December 5, 12, 19. Each workshop combines two technical lesson blocks, live coding and review. Course dates: November 14–December 25, 2026; final submissions and handover close December 25.

Sat, Nov 14
4:30 AM—7:00 AM (UTC)
Sat, Nov 21
4:30 AM—7:00 AM (UTC)
Sat, Nov 28
4:30 AM—7:00 AM (UTC)

Projects

24 hrs

Approximately 4 hours per teaching week: implement the AI pilot, run experiments, maintain delivery artifacts and prepare your final case study.

Async content

6 hrs

Approximately 1 hour per teaching week to review material, prepare the customer brief and incorporate feedback.

What you will take back to your next customer engagement

A complete delivery case study, built through a simulated customer engagement.

DISCOVER — A problem brief, stakeholder map, baseline and measurable acceptance criteria.

BUILD — A working AI pilot, architecture decisions and reproducible setup.

PROVE — Held-out acceptance tests, failure analysis and a value scorecard with explicit assumptions.

HAND OVER — Deployment instructions, an operations runbook, rollback plan and a customer-facing demo.

Each week adds an artifact to the same engagement. The final review assesses the quality of your engineering decisions and evidence, not just whether the demo looks impressive.

What learners say about earlier Vizuara programs

“It will definitely help change the way software developers work.”

Amartya · Senior Developer, Ericsson

Review of Vizuara’s Modern Software Engineering course.

This feedback describes a previous Vizuara program, not this new Maven FDE cohort. Read the original review and more learner experiences at https://reviews.vizuara.ai/

Vizuara: first principles, working code, proven teaching

Vizuara brings first-principles explanations, live coding, research papers and hands-on projects to a global AI learning community. Our YouTube channel has 224,000 subscribers, and our platform has more than 19,000 registered accounts across school, institutional and professional programs.

Founded by Purdue PhD Dr. Rajat Dandekar and MIT PhDs Dr. Raj Dandekar and Dr. Sreedath Panat, all IIT Madras alumni. The founders co-authored Manning’s Build a DeepSeek Model (From Scratch).

Vizuara’s collection of 109 learner stories reports a 4.96/5 average across rated reviews, with reviewers from organizations including Bosch, ISRO and Confluent. This feedback comes from earlier Vizuara programs.

Read learner stories: https://reviews.vizuara.ai/

Watch our teaching: https://www.youtube.com/@vizuara

More free lectures — coming soon

Harness Engineering: Introduction — with Dr. Rajat Dandekar. Free lecture coming soon.

Inference Engineering — free lecture coming soon.

Reinforcement Learning — free lecture coming soon.

We will add the recordings and lecturer thumbnails when the links are ready. These are upcoming resources; the lecture links are not available here yet.