Forward Deployed Engineer- Agentic AI

At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise‑scale impact, pairing leading class engineering with pod‑based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem‑solving, and client impact, this role puts you at the forefront of AI transformations.

As an Agentic AI FDE, you will design, build, and operationalize LLM‑powered systems that can reason, plan, retrieve information, use tools, and execute multi‑step workflows reliably. You will work on the "thinking layer" of AI systems: agent architecture, tool orchestration, memory and context management, retrieval pipelines, evaluation, and observability. You will help shape how complex domain knowledge is transformed into usable AI behavior, with a high bar for precision, traceability, and maintainability.

Additional responsibilities include:

• Embed with clients to identify business needs and translate high‑value GenAI use cases into solutions.
• Partner with leaders, product owners, architects, and engineers to align priorities and delivery.
• Lead working sessions to shape solutions and drive client outcomes.
• Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
• Contribute independently within an FDE pod while mentoring newer team members.

• Build AI‑enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
• Develop scalable AI engineering patterns, tool‑use approaches, and human‑in‑the‑loop controls.
• Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
• Deliver production‑quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
• Design extensible functionality, support sprint sizing, and align solutions with senior team members.
• Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.
• Strong understanding of memory and context management, including context windows, retrieval‑driven context assembly, persistent memory, and high‑signal token selection.
• Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade‑offs, and evaluation methods.
• Experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.

AI & Engineering leverages cutting‑edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission‑critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client’s unique requirements.

• Bachelor’s degree (or equivalent) in Computer Science, Data Science or Engineering.
• 3+ years of experience in software engineering, data engineering, data science, or analytics engineering.
• 1+ years of hands‑on experience building production‑grade applications with LLMs, including prompt design, tool use, structured outputs, error handling, and model behavior tuning.
• 1+ years of experience with Lang Chain and especially Lang Graph for orchestrating complex LLM workflows and agent behavior.
• 1+ years of experience designing and optimizing RAG systems end to end, including indexing, retrieval, reranking, grounding, and evaluation.
• 1+ years of experience with memory and context management, including context windows, retrieval‑driven context assembly, persistent memory, and high‑signal token selection.
• Deep understanding of how LLMs behave in practice, including strengths, failure modes, hallucination risks, reasoning limitations, latency/cost trade‑offs, and evaluation methods.
• 2+ years of experience with Python and modern software engineering practices, including testing, CI/CD, version control, and API integration.
• 1+…