Lead Forward Deployed Engineer, Microsoft AI & Data

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.

Recruiting for this role ends on 10/30/2026

Work you’ll do As a Lead Microsoft AI&Data FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte’s most strategic clients. You’ll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You’ll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte’s global delivery capability to maximize speed and scale.

Client Engagement

• Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
• Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
• Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision
• Represent Deloitte’s FDE capability in client pursuits, executive briefings, and platform partner engagements-contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

• Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
• Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates
• Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
• Mentor and develop junior FDEs

GenAI Solution Development

• Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below)
• Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
• Govern end-to-end RAG pipeline design-including ingestion, chunking, embedding, vector retrieval, and hybrid search-ensuring production-grade quality and scalability.
• Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

• Review and contribute to production-quality code
• Guide architecture of data pipelines powering GenAI use cases
• Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
• Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)

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.
• 7+ years of experience in software engineering, data engineering, data science, or analytics engineering.
• 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
• 1+ years of experience with Microsoft AI&Data including hands on experience with Azure AI Foundry
• 1+ years of experience leading project workstreams/engage…