Applied Scientist, GenAI
Core
Design and deploy production-grade GenAI and ML systems, specifically focusing on LLM/SLM optimization for agentic, multi-agent architectures in cloud environments.
Role type
Staff / Senior Applied Scientist (GenAI & ML Systems)
Builds
Multi-agent systems (planner/executor, tool-using agents, supervisor patterns), production ML models (SLMs), and Advanced RAG pipelines.
Domain
Life sciences and healthcare supply chain operations
Deliverable
production ML models | product features
Required skills
Context engineering for multi-agent systems, Fine-tuning SLMs, Advanced RAG implementation, Inference optimization, Vector DB and search stack experience, Full lifecycle management (research to production)
Preferred skills
Enterprise constraints management (privacy, security, governance), GenAI observability design, Python backend development, Cloud deployment (AWS/Azure/GCP), Scalable inference operations
Technologies
Python, AWS/Azure/GCP, Vector DBs, Cross-encoders, Speculative decoding
Responsibilities
Build and ship multi-agent systems from prototype to production, Write production-quality code for agent orchestration and tool integration, Lead context engineering strategies for multi-agent coordination, Fine-tune and deploy SLM models for production usage, Build Advanced RAG pipelines end-to-end, Implement evaluation frameworks for multi-agent systems, Optimize for cost and latency via model routing and caching, Mentor peers through code reviews and architecture sessions
Seniority
Staff / Senior, hands-on IC
