Lead Forward Deployed Engineer - Aws

Lead Forward Deployed Engineers (LFDE) at Deloitte help clients turn AI ambition into enterprise-scale impact by building and deploying GenAI solutions into production. This role involves leading engineering pods, architecting LLM-enabled applications, governing RAG pipelines, and defining evaluation frameworks, with a strong focus on client engagement and delivery.

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 FDE pods of 2–5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health
  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 _(see Platform Requirements below)_
  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

  • 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 AWS AI&Data including hands on experience with one of the following key platforms/products; Amazon Bedrock, Bedrock Agents, Knowledge Bases, Guardrails
  • 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

Nice to have

  • 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
  • Experience operating within hybrid onshore/offshore teams
  • Familiarity with security, privacy, and compliance considerations

What the JD emphasized

  • GenAI solutions into production
  • LLM-enabled applications
  • RAG pipeline design
  • evaluation frameworks

Other signals

  • GenAI solutions into production
  • LLM-enabled applications
  • RAG pipeline design
  • evaluation frameworks