Forward Deployed Engineer – Microsoft Ai&data

Deloitte Deloitte · Consulting

Deloitte is seeking a Forward Deployed Engineer (FDE) to help clients turn AI ambition into enterprise-scale impact by building and deploying GenAI-enabled solutions. The role involves working with clients to identify use cases, prototype and deliver AI solutions, build agentic platforms and workflows, and apply architecture decisions for quality, safety, latency, and cost. The engineer will also focus on developing scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls, delivering production-quality code with strong MLOps practices.

What you'd actually do

  1. Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
  2. Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  3. Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  4. Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  5. Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Skills

Required

  • Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
  • 3+ 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

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-enabled solutions
  • enterprise-scale impact
  • production-quality code
  • client or production environments

Other signals

  • GenAI-enabled solutions
  • enterprise-scale impact
  • production-quality code