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Lead Forward Deployed Engineer, Databricks 2026- US, UK

🕒 17 days ago
📍 🇺🇸 Atlanta - Remote🎺 Lead🤖 AI Engineer💼 Full-Time
Data EngineeringAI EngineeringPlatform EngineeringSolution Architecture
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📜 Description

  • Work directly with business and technical stakeholders to identify high-value data and AI use cases that can be delivered on Databricks.
  • Design, build, and deploy production-grade data and AI solutions using Databricks capabilities across the Lakehouse, Mosaic AI, Unity Catalog, and more.
  • Lead client discovery sessions to understand business workflows, data availability, platform maturity, and measurable success criteria.
  • Architect AI-native data platforms that support agentic workflows, semantic analytics, and operational applications.
  • Create prototypes, demos, and technical reference architectures that showcase the value of Databricks for enterprise AI.

🛠️ Requirements

  • Strong experience in data engineering, AI engineering, platform engineering, solution architecture, or enterprise software development
  • Hands-on experience with Databricks, Spark, Delta Lake, Lakehouse architecture, data pipelines, model deployment, or modern data platform patterns
  • Strong Python and SQL skills. Experience with PySpark, MLflow, Databricks Workflows, Unity Catalog, Databricks SQL, or similar tooling is strongly preferred
  • Familiarity with enterprise AI patterns such as RAG, agents, model serving, vector search, semantic layers, data applications, evaluation frameworks, and governance
  • Ability to work directly with clients, understand ambiguous business needs, and translate them into technical architecture and implementation plans
  • Strong communication skills with the ability to engage executives, business leaders, architects, data engineers, ML engineers, and analytics teams
  • Comfort moving from strategy to architecture to hands-on development
  • A practical understanding of what it takes to move from demo to production in complex enterprise environments
  • Databricks certification or deep hands-on delivery experience in the Databricks ecosystem
  • Experience building Databricks Apps, Genie rooms, Lakebase-backed applications, Mosaic AI workflows, feature pipelines, MLflow deployments, vector search systems, or agentic solutions
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