Lead Forward Deployed Engineer, Frontier GenAI

About the role

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.

Responsibilities

• 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.
  • Set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team.
  • Translate engineering trade-offs into clear decisions for client leaders when needed.
  • Influence decisions at the C-suite level, while maintaining hands-on technical credibility.
• 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

• Build trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders.
  • 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.

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.
  • Cross-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.
  • Define 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).

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 one of the following Frontier GenAI Platforms: Anthropic, Google or Open AI, including hands on experience with one of the following key platforms/products; Claude API, Claude for Enterprise, tool use, extended thinking, Claude Code, Gemini API, Vertex AI Agent Builder, Grounding, Google Workspace integration, GPT-4o, Assistants API, Responses API, OpenAI Agents SDK.
  • 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 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.

• Familiarity with security, privacy, and compliance considerations.