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Associate Forward Deployed AI Engineer

Coforge
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Associate Forward Deployed AI Engineer

 

 

Coforge (Princeton, NJ) · Cohort · Full-time

 

About the team 

The Coforge FDE Practice is the tip of the spear for our enterprise AI work. Our Forward Deployed AI Engineers embed with Fortune 500 clients across financial services, insurance, healthcare, travel, and retail to take AI from idea to production system that delivers the expected ROI.

 

You'll join a 60~90-days intensive training that turns you into a deployable FDE: a foundation in applied AI engineering (LLMs, RAG, agentic systems, evals), a focused vertical immersion, and a capstone built on a client-mirrored problem. You'll be paired with senior FDEs from day one — not lectured at, but pulled into real work the moment you can contribute.

 

About the role 

  • Embed with an enterprise client team — typically Fortune 500 — to deliver production AI systems in six-to-ten-weeks.
  • Translate ambiguous client problems into shipped solutions — most FDE work begins with a 

vague pain point, not a written spec.

  • Co-own a scoped workstream with a senior FDE: agentic workflows, RAG pipelines, eval frameworks, fine-tunes, or AI-native rebuilds of legacy processes.
  • Ship production code from day one.
  • Present work weekly to client stakeholders, ranging from engineering leads to the C-suite. Write the readout, demo the system, and defend design choices.
  • Move between clients and verticals.
  • Mentor the next cohort of associates as they arrive.

 

Must have skills

  • BS or MS in Computer Science, Mathematics, Physics, or a related quantitative field.
  • From one to three years of experience building production grade AI systems using LLMs. Understanding of RAG, embeddings, vector retrieval, and how LLM evals work.
  • Experience writing production-quality Python code, using Git, CI/CD, and testing frameworks in a team setting.
  • Experience using AI coding tools (e.g., Claude Code, Codex, Cursor) using a well-developed workflow (e.g., spec-driven development, loop-engineering).
  • Excellent written and verbal communication skills. Able to communicate technical decisions to a non-technical audience.

 

Nice to have skills

  • Hands-on experience with agentic frameworks (e.g., LangGraph, Anthropic SDK, OpenAI Agents SDK) and MCP.
  • Hands-on experience with eval and observability tooling (e.g., LangSmith, Weights & Biases)
  • Cloud certification or experience with AWS, Azure, or GCP
  • Experience with one of TypeScript, Go, Rust, C++, or OCaml.
  • Fine-tuning experience (LoRA, QLoRA, or full fine-tune).
  • Experience with reinforcement learning (PPO, DPO, or RLAIF)
  • Public technical footprint: blog, popular GitHub repos, Hugging Face contributions, or active technical writing.
  • Prior client-facing experience in any context — consulting, sales engineering, founder, tutoring, debate, teaching.
  • Familiarity with one of our focus verticals (banking, insurance, healthcare, travel, retail).