Senior ML & AI Technical Solutions Engineer
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will diagnose and optimize data, machine learning, and AI workloads in production. You will support model deployments, inference, monitoring, observability, and lifecycle operations. You will guide customers on generative AI applications, retrieval, agent systems, vector search, and prompt design, while contributing technical documentation and product improvements.
Requirements
- 8+ years designing, building, and scaling data, machine learning, and AI systems
- Python, Scala, and Java experience in production environments
- Machine learning or generative AI expertise
- Experience with AWS, Azure, or GCP
- Data engineering experience for end-to-end machine learning training pipelines
- Apache Spark experience
- Knowledge of feature engineering, ML frameworks, model monitoring, drift detection, and retraining
- Experience building, designing, or troubleshooting LLM-based generative AI applications
- Familiarity with LangChain or LangGraph
- Knowledge of MLOps and LLMOps
- Customer service skills
Responsibilities
- Act as a technical expert for data pipelines, ML pipelines, and AI applications
- Analyze and troubleshoot production workloads at the code level
- Optimize workloads for performance, reliability, latency, and cost
- Support machine learning and large language model deployments
- Guide customers on experiment tracking, model lifecycle, and observability
- Support generative AI use cases using LLMs, agents, RAG, APIs, and vector search
- Collaborate on product improvements and business growth
- Contribute technical documentation and teach AI systems new skills