> Markdown version of [/jobs/ext/2014302-lead-databricks-forward-deployed-engineer-gps](https://www.wearedevelopers.com/jobs/ext/2014302-lead-databricks-forward-deployed-engineer-gps). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Databricks Forward Deployed Engineer - GPS - **Company:** Deloitte T.T.L. - **Location:** Chicago, IL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Continuous Integration, Information Engineering, DevOps, Global Positioning Systems (GPS), Software Engineering, Large Language Models, Prompt Engineering, Information Technology, Machine Learning Operations, Data Pipelines, Databricks - **Published:** August 10, 2026 - **Apply:** https://us.experteer.com/career/view-jobs/lead-databricks-forward-deployed-engineer-gps-chicago-il-usa-58886893 ## About the Role and and phased deployment plans * Mentor junior engineers and maintain strong client-facing credibility * Coordinate multi-pod engagements and ensure consistent client experience * Review production-quality code and oversee data pipelines powering GenAI use cases Tasks * Bachelor's degree in Computer Science, Data Science or Engineering * 10+ years in software engineering, data engineering, data science, or analytics engineering * 6+ years building and deploying GenAI/LLM-powered solutions in production * 6+ years Databricks experience with Lakeflow Connect, Lakebase, Agent Bricks, Model Serving, Genie, or Databricks Apps * 6+ years leading project workstreams and translating business problems into AI solutions * 5+ years building reliable, maintainable code with CI/CD DevOps in Databricks * Ability to travel ~50% * Legal authorization to work in the United States without sponsorship * Professional Databricks certifications Key requirements * ## Description Experteer Overview In this Lead Databricks FDE role, you act as a senior engineering leader embedded with strategic clients to deliver GenAI solutions into production. You set technical direction, remove blockers, and contribute hands-on while guiding end-to-end delivery across onshore and offshore pods. You build trusted advisor relationships with client leaders and influence executive stakeholders to align on a shared vision. This is a strategic, high-impact position focused on scalable AI platforms and real-world business outcomes. Compensation / Benefits * Lead forward-deployed engineering pods, owning execution, resource management, and delivery health * Define and govern architecture for GenAI solutions including LLM-enabled apps and RAG pipelines * Set engineering standards: CI/CD, testing, documentation, risk management, quality gates * Guide prompt engineering, tool-use patterns, and human-in-the-loop controls * Engage with clients at executive levels to define success metrics and phased deployment plans * Mentor junior engineers and maintain strong client-facing credibility * Coordinate multi-pod engagements and ensure consistent client experience * Review production-quality code and oversee data pipelines powering GenAI use cases Tasks * Bachelor's degree in Computer Science, Data Science or Engineering * 10+ years in software engineering, data engineering, data science, or analytics engineering * 6+ years building and deploying GenAI/LLM-powered solutions in production * 6+ years Databricks experience with Lakeflow Connect, Lakebase, Agent Bricks, Model Serving, Genie, or Databricks Apps * 6+ years leading project workstreams and translating business problems into AI solutions * 5+ years building reliable, maintainable code with CI/CD DevOps in Databricks * Ability to travel ~50% * Legal authorization to work in the United States without sponsorship * Professional Databricks certifications Key requirements * ## Related Videos - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Implementing continuous delivery in a data processing pipeline](https://www.wearedevelopers.com/videos/73-implementing-continuous-delivery-in-a-data-processing-pipeline) - [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Got AI ideas but no money? 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