What is a Forward Deployed Engineer?
A Forward Deployed Engineer sits between the product and the customer. They build the AI, deploy it on the cloud, wire up the infrastructure and make it work for a real business. Leading AI companies hire for this role aggressively because it needs AI, DevOps and cloud skills in one person.
This is also the profile best suited to the Incred ownership model: one person who can take a product from idea to production on their own.
What you'll learn
- Build LLM, RAG and agentic AI applications
- Deploy them on AWS with production-grade infrastructure
- Automate everything with CI/CD, Docker, Kubernetes and Terraform
- Monitor, evaluate and keep AI systems reliable
- Work directly with customers to turn requirements into shipped software
- Own a product end to end, from idea to production
Courses included
>>> llm.invoke(prompt)LLM Engineering
AI · Part 1
AI Engineering 1: LLM Engineering with Frontier & Open-Source Models
agent.run(tools=[mcp])Agentic AI
AI · Part 2
AI Engineering 2: Agentic AI & MCP – Build Autonomous Agents
$ deploy --env=prod ai-appAI in Production
AI · Part 3
AI Engineering 3: AI in Production – Deploy, Evaluate & Scale
$ kubectl apply -f prod.yamlDevOps
DevOps
DevOps Engineering: Linux to Kubernetes in Production
$ aws lambda invokeAWS Developer
Cloud
AWS Certified Developer – Associate
Learning roadmap
6 modules · 21 topics · hands-on labs in every module
Phase 1: Foundations (Month 1–2)4 topics
- Linux, networking and Git
- Python for automation and AI
- Docker essentials
- Project: containerised API
Phase 2: AI Engineering 1: LLM Engineering (Month 2–4)4 topics
- Frontier and open-source models
- RAG with vector databases
- Fine-tuning with LoRA / QLoRA
- Project: knowledge-base assistant
Phase 3: DevOps (Month 4–6)4 topics
- CI/CD with GitHub Actions and Jenkins
- Kubernetes and Helm
- Terraform on AWS
- Prometheus, Grafana, Loki
Phase 4: AI Engineering 2: Agentic AI & MCP (Month 6–7)3 topics
- OpenAI Agents SDK, CrewAI, LangGraph, AutoGen
- Building MCP servers
- Project: multi-agent system
Phase 5: AWS Cloud (Month 7–9)3 topics
- Networking, IAM, compute, storage, databases
- Serverless: Lambda, API Gateway, SAM
- Well-Architected design and certification prep
Phase 6: AI Engineering 3: AI in Production (Month 9–10)3 topics
- Deploying AI apps on AWS and Vercel
- Evaluation, guardrails and observability
- Capstone: deploy an AI product at a 'customer site', end to end
Why it's worth it
| If bought separately | Fee |
|---|---|
| AI Engineering (3 courses) | ₹15,000 |
| DevOps Engineering | ₹12,000 |
| AWS Cloud | ₹12,000 |
| Forward Deployed Engineer track | ₹20,000 (save ₹19,000) |
Your mentor
Amit Singh
CTO, Incred Applications Pvt Ltd · IIT Delhi alumnus
Leads the global engineering team of incred.golf across Germany, London, Croatia, India and Denver. Ex-lead at LangChain; previously with NVIDIA and IBM.