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.

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. 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.

Responsibilities

• 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 create 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
  • 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
  • Maintain deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)

Requirements

• 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/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 program stakeholders in fast-paced, ambiguous delivery environments
• Data engineering experience with Spark, Airflow/dbt, streaming, data modeling, or ML/data science background in feature engineering, experimentation, or model evaluation
• Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
• Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
• Experience operating within hybrid onshore/offshore teams
• Familiarity with security, privacy, and compliance considerations

Pay

The wage range for this role takes into account a wide range of factors including skill sets, experience, training, licensure, certifications, and business needs. The disclosed range estimate has not been adjusted for geographic differentials. A reasonable estimate of the current range is $189,200 to $372,900. You may also be eligible to participate in a discretionary annual incentive program, subject to program rules, where awards depend on individual and organizational performance.