Responsibilities

Design and implement an evaluation strategy for AI agents within the Databricks environment.
Apply data science methodologies to assess model and agent performance, reliability, and business relevance.
Contribute to MLOps workflows supporting the development and deployment of AI agents.
Contribute to AI solution refinement through experimentation, validation, and optimization.
Prepare and deliver client demos, including presentation preparation, demo planning, and showcasing AI use cases.
Perform basic domain research (chemistry-related) to support demo preparation and solution demonstrations.

Requirements

Strong Python engineering skills, including writing production-quality code and tests.
Experience building and integrating REST APIs (e.g., FastAPI/Flask) for AI services.
Practical experience with LLM agents / agentic workflows, including state management, tool/function calling, iterative execution, and output validation/guardrails.
Familiarity with core ML tooling (e.g., NumPy, scikit-learn, PyTorch) sufficient to support experimentation and validation.
Experience working with data stores (SQL databases such as PostgreSQL/MySQL, object storage such as S3, and/or NoSQL).
Working knowledge of MLOps / LLMOps: experiment tracking, versioning, CI/CD for AI workflows, and deployment/monitoring basics.
Experience designing evaluation and QA frameworks for LLM/agent outputs (quality, accuracy, consistency), including offline test sets, metrics, and regression testing.
Hands-on experience building production LLM applications and agents (RAG, multi-step reasoning, tool/function calling, MCP).
Experience working with Databricks.
Experience working with Docker.
English level B2+.

Experience with cheminformatics (e.g., molecular fingerprints, chemical descriptors, RDKit, molecular similarity search).
Experience working on healthcare or life sciences projects.