Databricks Forward Deployed Engineer - Gps

This role focuses on building and deploying GenAI-enabled solutions and agentic platforms using Databricks for enterprise clients. The engineer will work side-by-side with clients to prototype and deliver high-impact solutions, focusing on scalable AI engineering patterns, tool-use, and human-in-the-loop controls.

What you'd actually do

  1. Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.
  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

  • 4+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 2+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 2+ years of experience with Databricks including hands on experience with one of the following key platform technologies; Databricks features for data engineering, data science, and analytics including Lakeflow Connect, Agent Bricks, and Databricks Apps
  • 2+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 2+ years of experience building reliable, maintainable, and well-documented code and CI/CD DevOps in Databricks
  • Ability to travel 50%, on average
  • Must be legally authorized to work in the United States without the need for employer sponsorship
  • Ability to obtain and maintain a US government security clearance

Nice to have

  • Databricks certifications
  • Experience with cloud environments (AWS, Azure, and/or Google Cloud)
  • 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
  • Familiarity with security, privacy, and compliance considerations in regulated enterprise environments

What the JD emphasized

  • GenAI-enabled solutions
  • Databricks
  • client-facing
  • production environments
  • security, privacy, and compliance considerations in regulated enterprise environments

Other signals

  • GenAI-enabled solutions
  • Databricks
  • client-facing