Senior ML & AI Technical Solutions EngineerActive

Databricks
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The opportunity

As a Senior ML and AI Technical Solutions Engineer, you play a critical role by helping customers debug and maintain stable GenAI and ML Workloads with AI agent systems using the Databricks Platform. You will develop product expertise end-to-end by advising a broad set of…

What you'll do

  • Act as senior technical solution expert for complex issues spanning data: pipelines, ML pipelines and/or AI applications, applying deep expertise in distributed systems.

  • Analyse and troubleshoot production workloads at the code level, optimise for: performance, reliability, latency, and cost.

  • Diagnose and support Machine Learning and/or Large Language Model: deployments, including real-time and batch inference, autoscaling, monitoring, logging, and alerting. Serve as a Subject Matter Expert guiding customers on experiment tracking, model registry, versioning, evaluation, labelling, tracing, and lifecycle observability.

  • Provide high-quality support by guiding customers in leveraging Databricks AI: to solve generative AI use cases & challenges, leveraging LLMs, MCP, AI Agents, RAG/Agentic RAG, APIs, vector embeddings, semantic search, Vector Search/Lakebase databases, context orchestration, memory management, and prompt engineering.

  • Collaborate with internal teams to influence roadmap, product improvements and support business growth.

  • Develop expertise in productionizing systems in Databricks and share your: knowledge by contributing to wikis and other technical documentation, or by teaching our AI systems new skills, which will be used internally and externally by customers and partners.

What they're looking for

  • SME knowledge in feature engineering, ML frameworks, model training, model: monitoring, drift detection, and retraining strategies. Proficient in working with algorithms and deep learning, along with NLP techniques.
  • Prior experience building, designing or troubleshooting LLM-based Generative: AI applications. Familiarity with agentic frameworks (e.g., LangChain, LangGraph etc). Expertise in context orchestration, including prompt design, memory management, retrieval systems, vector embeddings, semantic search, and tool integrations.
  • Comprehensive Knowledge of MLOps and LLMOps with expertise in model: evaluation, scoring, ranking, optimisation, training, validation, and packaging.
  • Experience developing agent skills, plugins, and debugging with native AI capabilities is a plus.