We're looking for a Lead AI Engineer to take a chemically-aware AI assistant from prototype to production. The platform helps medicinal chemists interact with research, assay, and compound data through natural-language workflows, and your job is to evolve it into a scalable, evaluation-driven multi-agent system. Partnering with our technical leads and the chemists who use it, you'll drive the agent architecture, scientific tool integration, deployment and observability, and the evaluation framework that keeps the system trustworthy as it grows — plus support for chemistry-focused data workflows.

Responsibilities: Design and build multi-agent systems that integrate scientific tools, computational workflows, MCP-compatible services, APIs, and cheminformatics libraries such as RDKit.

Build containerized deployment pipelines (Docker) with proper observability (Langfuse), logging, and lifecycle management.

Collaborate on the development of agent evaluation frameworks, including automated testing, benchmarking, and performance monitoring.

Develop data preparation and engineering pipelines across structured, semi-structured, and unstructured sources, working alongside the data team's stack (Snowflake, Airflow, DBT, PostgreSQL, Oracle).

Contribute to CI/CD workflows and maintain code quality through code reviews,



modular design practices, and technical documentation.

Follow architecture, security, and engineering patterns established by the team.

What we expect: Robust Python software engineering.

Experience building applications based on LLMs, tool-calling, and agent frameworks.

Experience developing multi-agent or workflow-oriented AI systems.

Strong, hands-on experience with RDKit and associated chemoinformatics tooling

Experience with evaluation frameworks, automated testing, and performance benchmarking.

Docker and containerized application development expertise.

Comfortable working with databases and data engineering workflows.

Ability to collaborate in a cross-functional scientific environment.

Availability to work until at least 1:00 PM EST.

Nice to have: Experience with Langfuse or another AI observability platform.

Snowflake Cortex and/or Amazon Bedrock experience.

AWS ECS/Fargate deployment experience.

Data engineering stack experience: Snowflake, Airflow, DBT, Oracle.

GitLab-based development practices experience. We offer:

Strong management and technical expertise.

Contract till the end of the year with possible extension based on project needs and performance.

📌 Lead AI Engineer with experience in RDKit (Canada)
🏢 Quantori
📍 Canada