Lifebit is a federated biomedical data platform helping researchers access sensitive data without moving it. As an AI Engineer, you will build and deploy machine learning models and autonomous AI agents for clinical and genomic research. The role focuses on LLMs, RAG pipelines, and agentic architectures within privacy-critical environments. It requires 2+ years of AI/ML engineering experience and expertise in Python, TypeScript, and modern ML frameworks.
Responsibilities
Design and implement autonomous AI agents using LangGraph, CrewAI, or AutoGen for complex scientific queries.
Build and optimize Advanced RAG pipelines integrating structured clinical data and scientific literature.
Create tools for agents to safely interface with federated APIs, SQL databases, and bioinformatic engines.
Fine-tune LLMs for function-calling and tool-use accuracy in life sciences.
Develop evaluation frameworks (LLM-as-a-judge) and human-in-the-loop patterns for safety.
Scale agentic workloads in production using Kubernetes.
Requirements
BSc/MSc in Computer Science, AI, Machine Learning, or a quantitative field (PhD preferred).
2+ years of hands-on AI/ML engineering experience building validated products.
Deep proficiency in Python and TypeScript; experience with PyTorch, TensorFlow, JAX, or Scikit-learn.
Proven experience with LLMs, including fine-tuning and RAG architectures.
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