AI Engineer - FDE; Forward Deployed Engineer - Public Sector

Job : CSQ
326R87

PLEASE NOTE: This position requires eligibility to work in the United States and the ability to obtain and maintain a U.S. Government security clearance at the Top Secret level or higher, as required by applicable federal law and contract terms. Due to specific federal contract requirements and client site access obligations for this role, U.S. citizenship is a condition of employment
. The role is based in the Washington, D.C., Maryland, or Virginia metropolitan area to support periodic on‑site work and client collaboration. Candidates with an active Top Secret clearance or higher are strongly encouraged to apply.

The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team  deliver professional services engagements to help our customers build and product ionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team.

This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap

• Experience building GenAI applications, including RAG, multi‑agent systems, Text2
  
  SQL, fine‑tuning, etc., with tools such as Hugging Face, Lang Chain, and DSPy
• Expertise in deploying production‑grade GenAI applications, including evaluation and optimizations
• Extensive years of hands‑on industry data science experience, leveraging common machine learning and data science tools (i.e., pandas, scikit‑learn, PyTorch, etc.)
• Experience building production‑grade machine learning deployments on AWS, Azure, or GCP
• Experience communicating and/or teaching technical concepts to non‑technical and technical audiences alike
• Passion for collaboration, life‑long learning, and driving business value through AI
• [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large‑scale distributed datasets

$161,280 — $225,792 USD

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non‑commissionable roles or on‑target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job‑related skills, depth of experience, relevant certifications and training, and specific work location.

Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here .

$161,280 — $225,792 USD

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

To learn more, follow Databricks on Twitter, Linked In and Facebook.