Lead Forward Deployed Engineer, Frontier Genai

Deloitte Deloitte · Consulting

Lead Forward Deployed Engineer for GenAI solutions at Deloitte, focusing on enterprise-scale impact. This role involves leading engineering pods to develop and deploy GenAI solutions into production for strategic clients, setting technical direction, and ensuring delivery standards. Responsibilities include architecting LLM-enabled applications, governing RAG pipelines, defining evaluation frameworks, and overseeing data pipelines and cloud environments.

What you'd actually do

  1. Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
  2. Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
  3. Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms
  4. Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability.
  5. Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Skills

Required

  • 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 Op
  • Cloud environments (AWS, Azure, and/or Google Cloud)

Nice to have

  • prompt engineering
  • tool-use patterns
  • human-in-the-loop controls
  • production-quality code
  • data pipelines powering GenAI use cases
  • data management
  • testing
  • CI/CD
  • logging
  • versioning
  • documentation practices

What the JD emphasized

  • 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 Op

Other signals

  • client-facing
  • production deployments
  • LLM-enabled applications
  • RAG pipeline design
  • evaluation frameworks