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. Work you'll do 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: Client Engagement • 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. Solution Engineering • 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. The team 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. Required Qualifications • 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 LangChain and especially LangGraph 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+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions • 1+ years of experience building reliable, maintainable, and well-documented code • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve • Limited immigration sponsorship may be available Preferred Qualifications • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking) • Demonstrated ability to work directly alongside client technical teams and pro