Software Engineers
Moving from building features to owning AI delivery with customers.
FLAGSHIP PROGRAMStarts 5 Dec 2026
Forward Deployed
Engineering.
Own the outcome.
Forward Deployed Engineering Practitioner Program
Become the engineer who takes AI from a promising prototype to a working system inside a real organisation.
WHAT AN FDE BLENDS3 ROLES
Three roles. One engineer.
52Planned sessions
1Connected engagement
OnlineOptional Campus Immersion
IIT Hyderabad
THE FIRST PROGRAM
A new chapter starts here.
ENROLLING NOW5 December 2026 – 27 February 2027
Online learning + optional campus immersions. Final timetable to be confirmed.
See the scheduleWHAT CHANGES FOR YOU
Build it. Deliver it. Own it.
01
Discover the real problem
Map the customer workflow, constraints and success measures before choosing the technology.
02
Engineer the complete system
Build services, data flows, AI workflows and enterprise integrations in one engagement.
03
Own deployment and adoption
Release safely, operate reliably and connect user adoption to measurable business value.
ELIGIBILITY
Who joins this program
Bring your engineering foundation. Build towards delivery ownership — from understanding a customer’s problem to deploying and handing over a working AI solution.
AI / ML & Data Engineers
Who can build models, pipelines and prototypes, and want them running in production.
Platform, DevOps & Analyst
Hands-on technologists stepping into customer-facing AI delivery.
Teams from one company
Taking AI pilots to production together. [Team terms]
Typical experience: 2–3+ years
Python
Write, read and debug app code; packages, exceptions, structured data.
APIs & Services
HTTP, REST and JSON; FastAPI and Pydantic.
Databases
SQL, tables, keys and basic transactions in Postgres.
Engineering Workflow
Git, Linux terminal, dependencies, existing repos.
Testing & Containers
Basic pytest; running a containerised app.
THE ENGAGEMENT IS THE SPINE
A deployment you can explain. Evidence you can show.
Every lab adds to one engagement repository, from customer discovery to operational handover.
Discovery & scope
Customer brief, workflow map, requirements, risks and an outcome hypothesis.
Engineering foundation
Reproducible repository, typed service, versioned API, data layer, tests and CI.
Enterprise intelligence
Integrations, event processing, model routing, retrieval and agent workflows with human control.
Operational product
Role-aware operator interface, deployment architecture, enterprise identity and network fit.
Production evidence
Tracing, service-level objectives, evaluation harness, threat model and failure exercises.
Forward deployment
Staged rollout, adoption and value measurement, handover, executive readout and postmortem.
The capstone connects the customer workflow, architecture, integrations, failure modes, evaluation, operations, adoption and business outcomes. A chat interface alone does not meet the engagement standard.
THE LEARNING BLUEPRINTOne engagement.
One engagement.
Every layer of delivery.
Each module adds evidence to the same engagement repository. The curriculum is a draft; the session-to-date mapping is being finalised.
52 PLANNED SESSIONS · GUIDED LABS + CAPSTONE
01SESSIONS 01–04Customer discovery and value definition
Customer discovery and value definition
Customer interviews, workflow mapping, opportunity selection, baselines, measurable acceptance and engagement scope.
02SESSIONS 05–08Solution and service foundation
Solution and service foundation
Architecture and trust boundaries, codebase navigation, API contracts, transactional persistence, tests and continuous integration.
03SESSIONS 09–12Data estate and canonical model
Data estate and canonical model
Source profiling, canonical schemas, entity resolution, reconciliation, data quality and source-to-record lineage.
04SESSIONS 13–16Integrations and event processing
Integrations and event processing
Customer-system adapters, retries, idempotency, Kafka events, replay and incremental synchronization.
05SESSIONS 17–20LLM interaction and safe tool execution
LLM interaction and safe tool execution
Model interfaces, prompt and context design, structured outputs, schema validation and controlled tool execution.
06SESSIONS 21–24Enterprise retrieval and grounding
Enterprise retrieval and grounding
Document ingestion, hybrid search, reranking, evidence-grounded answers, access filters and knowledge freshness.
07SESSIONS 25–28Durable agents and operator control
Durable agents and operator control
Workflow state, LangGraph orchestration, checkpoints, memory, human approvals, operator experience and MCP integration.
08SESSIONS 29–32System evaluation and model economics
System evaluation and model economics
Evaluation datasets, failure analysis, judge calibration, model comparisons, cost and latency trade-offs, regression gates and release selection.
09SESSIONS 33–36Identity, security and governance
Identity, security and governance
Enterprise identity, authorization, identity propagation, AI threat testing, privacy controls and governance evidence.
10SESSIONS 37–40Deployment and runtime architecture
Deployment and runtime architecture
Customer deployment topologies, containers, configuration, secrets, networking, infrastructure automation and release delivery.
11SESSIONS 41–44Observability and operational reliability
Observability and operational reliability
End-to-end telemetry, service-level objectives, actionable alerts, capacity testing, failure diagnosis and incident recovery.
12SESSIONS 45–48Customer pilot and cutover
Customer pilot and cutover
User acceptance testing, pilot design, migration readiness, go-live decisions, cutover, rollback and adoption feedback.
13SESSIONS 49–52Handover and engagement defence
Handover and engagement defence
Operational handover, measured business value, technical and executive defence, reusable assets and field-to-product feedback.
THE LEARNING BLUEPRINTOne engagement.
One engagement.
Every layer of delivery.
Each module adds evidence to the same engagement repository. Build one customer AI engagement from problem discovery to tested deployment, user acceptance and handover.
PHASE 1Discover and designWEEKS 1–2
SESSIONS 01–08WEEK 01 · SESSIONS 01–04Customer discovery and value definition
Customer interviews, workflow mapping, opportunity selection, baselines, measurable acceptance and engagement scope.
WEEK 02 · SESSIONS 05–08Solution and service foundation
Architecture and trust boundaries, codebase navigation, API contracts, transactional persistence, tests and continuous integration.
PHASE 2Connect enterprise data and systemsWEEKS 3–4
SESSIONS 09–16WEEK 03 · SESSIONS 09–12Data estate and canonical model
Source profiling, canonical schemas, entity resolution, reconciliation, data quality and source-to-record lineage.
WEEK 04 · SESSIONS 13–16Integrations and event processing
Customer-system adapters, retries, idempotency, Kafka events, replay and incremental synchronization.
PHASE 3Build and validate the AI workflowWEEKS 5–8
SESSIONS 17–32WEEK 05 · SESSIONS 17–20LLM interaction and safe tool execution
Model interfaces, prompt and context design, structured outputs, schema validation and controlled tool execution.
WEEK 06 · SESSIONS 21–24Enterprise retrieval and grounding
Document ingestion, hybrid search, reranking, evidence-grounded answers, access filters and knowledge freshness.
WEEK 07 · SESSIONS 25–28Durable agents and operator control
Workflow state, LangGraph orchestration, checkpoints, memory, human approvals, operator experience and MCP integration.
WEEK 08 · SESSIONS 29–32System evaluation and model economics
Evaluation datasets, failure analysis, judge calibration, model comparisons, cost and latency trade-offs, regression gates and release selection.
PHASE 4Secure, deploy and operateWEEKS 9–11
SESSIONS 33–44WEEK 09 · SESSIONS 33–36Identity, security and governance
Enterprise identity, authorization, identity propagation, AI threat testing, privacy controls and governance evidence.
WEEK 10 · SESSIONS 37–40Deployment and runtime architecture
Customer deployment topologies, containers, configuration, secrets, networking, infrastructure automation and release delivery.
WEEK 11 · SESSIONS 41–44Observability and operational reliability
End-to-end telemetry, service-level objectives, actionable alerts, capacity testing, failure diagnosis and incident recovery.
PHASE 5Launch, transfer and demonstrate valueWEEKS 12–13
SESSIONS 45–52WEEK 12 · SESSIONS 45–48Customer pilot and cutover
User acceptance testing, pilot design, migration readiness, go-live decisions, cutover, rollback and adoption feedback.
WEEK 13 · SESSIONS 49–52Handover and engagement defence
Operational handover, measured business value, technical and executive defence, reusable assets and field-to-product feedback.
The curriculum and schedule are subject to enhancements. A few modules, topics or their sequence may change as the programme is enriched, while retaining the core learning objectives.
MAKE SPACE FOR YOUR NEXT STEP
TENTATIVEThe journey, on your calendar.
05 Dec 2026Program beginsOptional campus immersion
12, 19, 26 DecTeaching weekends & guided labsOnline learning
09, 16, 23 Jan 2027Teaching weekends & guided labsOnline learning
30 Jan 2027Campus touchpointOptional campus immersion
06, 13, 20 FebTeaching weekends & guided labsOnline learning
27 Feb 2027Final milestoneConvocation planned
12 dated weekends are listed in the tentative calendar. The 52 curriculum sessions will be mapped to dates in the final timetable. No session is listed for 2 January. View the tentative calendar ↗
ACADEMIC DEPTH. FIELD EXPERIENCE.
Meet your instructors.
BEYOND THE SLIDES
A room full of possibility.
Questions, conversations and the
quiet satisfaction of making something work.
In the classroomPhotos from the first cohort will appear here.
Building togetherPhotos from the first cohort will appear here.
The moments that matterPhotos from the first cohort will appear here.
MARK THE WORK YOU HAVE DONE
Evidence you can stand behind.
Certificate details and completion requirements will be announced with the final program release.
- A customer brief and architecture decisions
- Working software, integrations and operational evidence
- A rollout, adoption and handover portfolio
The final certificate title, issuer details and assessment criteria are to be confirmed.
OFFICIAL REGISTRATION & ENQUIRIES
Start with a conversation.
The FDE registration form has not been provided yet. Contact the program team about the first program.
Enquire on this page No registration or payment is collected on this page.
YOUR NEXT CHAPTER
Take ownership of what AI can do.
For AI engineers and software engineers with roughly 2–5 years of experience, moving into customer-facing AI delivery.
Before you begin
Working programming skills, API familiarity and readiness to build and review software. This is a practitioner program, not a first course in coding.
DECEMBER PROGRAM INFORMATION
Earlybird Price: ₹90,000 + taxesLimited seats
Standard Price
₹1,00,000 + taxes
Early-bird price applies to the first 20 registrations.
Runs from 5 December 2026 – 27 February 2027.
- All sessions and guided labs (Online with Optional Campus Immersion)
- IIT Hyderabad certificate
What does a Forward Deployed Engineer do?
Works with customers to understand a business problem and deliver a working solution, combining AI and software engineering with discovery, integration, deployment and stakeholder communication.
Do I need prior GenAI experience, and which technologies will I use?
No prior GenAI or ML experience is needed, only solid software engineering foundations (see Eligibility). You will learn LLMs, retrieval, agents and evaluation hands-on, using a Python-first, open-source stack that includes FastAPI, Postgres, LangGraph, Qdrant, Ragas, authentik, Docker, OpenTelemetry and OpenObserve.
Is it relevant for cybersecurity, DevOps, data or integration professionals?
Yes, if your role is hands-on and you meet the prerequisites. Identity, secure integrations, data pipelines, deployment and observability are core to the curriculum.
What will I build?
A team capstone: an enterprise AI workflow with evidence retrieval, human approval and an audit trail, taken from discovery through deployment and handover.
Do I need paid AI subscriptions?
No. The required labs use programme-provided scripts and free tiers. Optional experimentation outside this may cost extra.
How will I be assessed?
Through working software, tests, evaluation results, architecture and security evidence, and a capstone presentation where you defend your decisions. Certificate requirements will be announced separately.
How long is the programme, and can I attend while working full-time?
Yes, the programme is designed for working professionals. It runs for 13 weeks, with 52 sessions and 156 live hours. Each session includes a 90-minute lecture and a 90-minute lab. Plan for about 12 live hours a week, plus independent time for assignments and the capstone.