Mentorship to Internship Programin Forward Deployed AI Engineering
Build, deploy, and own production AI systems. 6, 9, or 12 months. 18 hours a week. Built by NetPy on the DevByte platform.
Built under BITSian mentorship — engineering rigour, not EdTech marketing.
1M+
AI jobs unfilled in India by 2026
40%
Year-on-year AI hiring growth
<3%
Grads who can build production AI
The role
What is a Forward Deployed Engineer?
A Forward Deployed AI Engineer works directly with customers to solve high-stakes AI projects — and ships solutions that actually run in production.
The role sits at the intersection of software engineering, AI/ML, and production systems. Generic courses don’t produce FDEs — which is exactly why demand is outpacing supply.
Platform Engineer
Choose the right stack, integrations, and deployment path.
Software Engineer
Write production-grade code and ship to cloud infrastructure.
Solutions Architect
Translate business needs into technical solutions customers pay for.
Live System Owner
Deploy, monitor, and improve AI systems in production.
The work cycle
How an FDE actually operates
A continuous loop. Bridging business needs and AI execution — repeated until the system is owned end-to-end.
Discover
Understand the business reality — how operations actually work, not just the stated requirement.
Define
Quantify the problem, identify decision bottlenecks, and align stakeholders on success metrics.
Design
Choose the right AI approach, model stack, and integration path for production constraints.
Build
Write production-grade code, integrate APIs, and implement evaluation frameworks.
Deploy
Ship to production with monitoring, retries, idempotency, and security guardrails.
Improve
Monitor usage, iterate on failure modes, and make the system self-improving over time.
The gap
90%+ of AI pilots fail at the last mile.
AI doesn’t fail in demos. It fails after launch — at integration, deployment, monitoring, and adoption.
Forward Deployed Engineers exist to close that gap. They are the people who make AI work inside real client stacks.
Why production AI fails
Engineers, not models, are the bottleneck.
Where pilots break
Indicative failure modes
The FDE Journey
Six months. Six live AI products.
Every learner completes the same core. The 9- and 12-month tracks extend it with Loop Engineering, job-readiness, and managed placement.
Dev Core
Python, Git, FastAPI, Docker, AWS basics. Graduate with a deployed REST API.
Data & ML
Pandas, scikit-learn, model training, evaluation. Serve a trained ML model via API.
LLMs & GenAI
Prompt engineering, embeddings, RAG systems. Build a document Q&A chatbot.
Agentic AI
LangChain, LangGraph, multi-agent orchestration. Deploy a 3-agent workflow.
Production AI
MLOps, CI/CD, monitoring, security guardrails. Production app with live telemetry.
Capstone
Full product build, portfolio, demo day. Own an end-to-end AI product.
Choose your track
Three tracks. One shared core.
Every FDE starts with the same 6-month core. Longer tracks add Loop Engineering, job-readiness, and managed placement.
24 weeks · ~432 hrs
6-Month FDE Core
From zero to shipping AI. The shared foundation every FDE completes.
- 6 live deployed AI products
- Dev Core → Data & ML → LLMs → Agentic AI → Production → Capstone
- Self-driven GitHub/LinkedIn portfolio
- DevByte platform access
- Portfolio-only placement
36 weeks · ~648 hrs
9-Month FDE
Everything in the core, plus Loop Engineering and interview mastery.
- Everything in 6-Month Core
- Loop Engineering: agent → verification → event-driven → hill-climbing loops
- Technical communication & personal brand
- Interview mastery + mock placement
- Verified Placement Portal profile
48 weeks · ~864 hrs
12-Month FDE
Full managed placement support through the NetPy Placement Portal.
- Everything in 9-Month FDE
- Placement Portal onboarding (Tier 3)
- Industry capstone with real-world brief
- Active placement → interviews → offer
- Lifetime alumni profile
DevByte Platform
Build on the same stack that ships production AI
DevByte is NetPy’s proprietary AI engineering platform — the same environment our engineers use for enterprise clients in healthcare, SaaS, logistics, and fintech.
Live IDE
Code, test, and iterate inside the same environment NetPy engineers use for real client projects.
Agent Builder
Design, wire, and debug multi-agent workflows with visual graph editing.
Deploy Engine
Ship to real cloud infrastructure — Docker, CI/CD, and public URLs.
Monitor Dashboard
Track latency, cost, errors, and model behavior for every deployed AI system.
Project Board
Cohort collaboration, weekly builds, and peer review in one place.
Portfolio Showcase
A verified public portfolio linked to GitHub and LinkedIn.
The FDE Method
Learn. Apply. Build. Repeat.
Learn
Short, focused lectures and readings on the DevByte platform — concepts first, tools second.
Apply
Hands-on labs where you implement the concept immediately on real datasets and APIs.
Build
Weekly projects shipped as pull requests. Every Saturday, a new live artifact is deployed.
Review
Live code reviews and mentor feedback from NetPy engineers who ship production AI.
Tool Stack
Production tools, not toy tutorials
Every tool is taught inside a deployment scenario on DevByte. Open-source first, with local models via Ollama so LLM work can be done at zero API cost.
Why NetPy
We build what we teach
We are not a training company that pivoted to AI. We are an AI engineering company that built a training methodology because we had to — to scale our own team fast enough to keep up with client demand.
Built under BITSian mentorship — engineering rigour, not EdTech marketing.
15+
Active engineers on live projects
35+
Engineers onboarded via DevByte internally
10+
AI products live in the market
3 yrs
Shipping production AI
Proof in production
The same engineers who built these systems teach your FDE cohort.
Anvayaa
HealthTechAI-powered dementia care companion with real-time behavioural pattern detection and caregiver alert routing.
SEOByte
SaaS / MarTechLangGraph multi-agent pipeline replacing a 40-hour manual SEO workflow with a 12-minute automated process.
NinjaHire
HR TechSemantic candidate matching engine that screens 10,000 applications in the time a human reads 10.
Piaxis
Architecture / AECLangGraph agent pipeline for auto-generation of architectural working drawings from 10,000+ specs.
The Mentors
Built and Mentored by practicing FDEs
Every module is designed by engineers who ship AI in production. The curriculum reflects how the role is actually practised — not how it's talked about.
Engineering leader focused on shipping AI systems that actually run in production.
Birla Institute of Technology and Science, Pilani
M.Sc. (Hons.) in Mathematics and B.E. (Hons.) in Chemical Engineering
2008 – 2013
Most AI products never make it to production. After shipping 20+ that actually work, I know why and how to fix it. I'm Co-Founder of ThinkByte AI, where we turn AI pilots into production systems that deliver measurable ROI. Over my 10+ years at Sequoia-backed Belong, KiraakFoods, and now ThinkByte, I've learned what separates AI demos from AI that ships. Our track record includes AI-powered dementia care at Anvayaa, SEOByte with 860–3,080% traffic growth, and NinjaHire with 90% recruitment automation. We're now building the ThinkByte Developer Platform to solve context engineering — the biggest bottleneck in AI-driven development.
15+ years building production data, ML and AI systems across BT and Shell.
Liverpool John Moores University
Master's degree, Machine Learning & Artificial Intelligence
Sep 2022 – Feb 2025
Data Scientist focused on Generative AI and Agentic Systems. I’ve spent 15 years building things that actually work in production — not just in notebooks. For the last few years my focus has been Generative AI: RAG pipelines, multi-agent systems, LLM evaluation, guardrails and observability. At BT, I led the team that built a document intelligence system processing 100K+ contracts and cut manual extraction time by 70%. At Shell, predictive maintenance models I built reduced equipment downtime by 25% across five oil refineries. I’m most interested in what happens after the model is built — evaluation frameworks, CI/CD gates, monitoring and drift detection — the stuff that separates a reliable system from one that slowly goes wrong while nobody’s watching. Stack-wise: Python, LangChain, LangGraph, CrewAI, AWS Bedrock/SageMaker/Textract, Docker, MLflow, FastAPI. Enough MLOps to know what breaks in production and why.
Product and strategy operator with elite engineering and business credentials.
XLRI Jamshedpur
Master of Business Administration (M.B.A.), Business Management
2015 – 2017
10+ years in Fintech and enterprise SaaS — working across Product, Sales, and Implementations across EMEA and NA — taught me one thing: Execution wins over Promises. Before co-founding ThinkByte AI, I spent years helping financial institutions and enterprises modernize with technology. Across hundreds of sales and boardroom conversations, I've seen the same themes repeat: enterprise buyers have been burned by overpromising vendors, nearly 70% of AI pilots never reach production, and smaller vendors often miss the nuances of enterprise operations. That's why at ThinkByte we lead with discovery, design low-risk high-clarity engagements, and build multi-stakeholder buy-in before a single line of code is written.
Free 2-Hour FDE Workshop
Three hands-on masterclasses with the mentors who built the program. No cost, no fluff.
Production AI
Ship models that stay reliable in production.
RAG & Agents
Build retrieval pipelines and agentic systems.
FDE Career
Map the path into forward-deployed engineering.
Outcomes
Built for a career, not just a certificate
300+
Hours of hands-on training
Real projects, real deployments, real debugging.
6
Live deployed products
₹18L
Starting salary for AI roles
<3%
Of grads are production-ready today
That gap is exactly why FDEs are in demand.
FDE for colleges
Bring the DevByte FDE program to your institution. Semester-integrated, co-branded certification, and NEP 2020-aligned — without building a curriculum in-house.
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Get the day-by-day teaching plan, project deliverables, and track comparison.
- 6-month, 9-month, and 12-month track breakdowns
- Weekly project deliverables and rubrics
- Tooling stack and deployment checklist
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An engineer who builds, deploys, and owns AI systems directly with customers — bridging software engineering, AI/ML, and production systems.
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