Associate Forward Deployed Engineer

Associate Forward Deployed Engineer Key Skills - LLMs, Agentic AI, Production AI, RAG, Python, Cloud & APIs Experience - 0 - 2 years Location - Princeton, NJ Education - BS or MS in Computer Science, EE, Math, Statistics, Physics, or a comparably rigorous STEM field. Candidates graduated from Computer Science program from US Tier-1/Ivy League universities are preferred. Recent graduates from the US Universities are encouraged About the Team - The Coforge Momentuum blue is at the forefront of enterprise AI innovation, helping Fortune 500 organizations transform AI from promising ideas into scalable, business-critical solutions. Our Forward Deployed Engineers work directly with leaders across Financial Services, Insurance, Healthcare, Travel, and Retail, solving complex challenges and delivering measurable business outcomes. As we rapidly expand our AI-first practice, we're looking for ambitious FDEs who want to be part of a high-growth team shaping the future of AI adoption. About the role: You'll join a 60~90-day intensive training program 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. What you'll do after the 90-day academy: Embed with an enterprise client team — typically Fortune 500 — to deliver production AI systems in two-to-four-week sprints. 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. Not slideware, not "POCs that never leave Jupyter" — real systems with real users. Present your work weekly to client stakeholders, ranging from engineering leads to the C-suite. You'll write the readout. You'll demo the system. You'll defend the design choices. Translate ambiguous client problems into shipped solutions — most FDE work begins with a vague pain point, not a written spec. Move between clients and verticals as you grow — typical glide path is solo workstream ownership by month nine, leading a deployment by month eighteen. Mentor the next cohort of associates as they arrive. What you'll need (mandatory): Languages: Production-quality Python. Comfortable reading any modern language Applied AI: Have built non-trivial applications using LLM APIs (Anthropic, OpenAI, Google). Understand RAG, embeddings, vector retrieval, and how evals work AI-native development: Daily user of AI coding tools (Cursor, Claude Code, Copilot, or equivalent) — not occasional, daily. Engineering hygiene: Git, basic CI/CD, Docker. You've worked in a codebase larger than your own Written communication: You can write a one-page memo a busy executive will actually read. Verbal communication: You can present a technical decision to a non-technical audience. Tested in our interview via a live project walkthrough Nice to have: Hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen, Mastra, OpenAI Agents SDK, MCP).Fine-tuning experience (LoRA, full fine-tune, RLHF basics).Eval and observability tooling (LangSmith, Braintrust, Arize, Weights & Biases) Cloud certification or strong hands-on experience with AWS, Azure, or GCP. TypeScript or Go alongside Python. 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) — but we explicitly do not require it; we train it. Show more Show less