Principal AI Engineer - Evinova

About the role

As Principal AI Engineer, you'll design and implement sophisticated agentic AI systems that power next-generation life sciences solutions. Working at the intersection of AI research and real-world healthcare applications, you'll build intelligent agents that can reason, plan, and act autonomously to solve complex clinical challenges.

Responsibilities

• Lead challenging projects in agentic AI, LLM orchestration, and multi-agent systems
• Build AI agents that directly impact clinical trials, drug discovery, and ultimately patient care
• Collaborate with product teams, clinical experts, and ML engineers in a fast-paced environment
• Develop automated evaluation systems, prompt optimization techniques, and advanced agent architectures
• Contribute to the AI in life sciences community through publications, conferences, and open-source work

Example impactful projects

• Intelligent AI agents for clinical document generation
• Advanced search systems for medical research
• Clinical trial optimization tools
• Synthetic patient data generation
• Multi-modal healthcare AI assistants

Essential Skills/Experience

• Master's Degree in a relevant field (such as mathematics, computer science, data science)
• 4+ years of industry experience in applied machine learning, with a strong focus on deep learning, NLP, and generative AI
• Extensive prior experience exploring and testing language model behavior, prompting, and building products with language models
• Expert knowledge of Python and advanced ML/LLM frameworks (e.g., TensorFlow, PyTorch, Google ADK, Crewai, LangChain, LlamaIndex)
• Extensive experience with AWS services (e.g. SageMaker, Bedrock, MSK, EKS, ECS, OpenSearch)
• Deep understanding of agentic AI systems and frameworks (e.g. agentic design patterns, multi-agent systems, reinforcement learning)
• Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences

Desirable Skills/Experience

• Ph.D. in a relevant field (such as mathematics, computer science, data science)
• Demonstrated technical leadership experience, including successful delivery of large-scale AI projects
• Experience developing complex agentic systems using LLMs
• Experience with low-level languages used for implementing high-performance ML code (C/C++, Rust, CUDA, etc.)
• Contributions to open-source AI projects or development of proprietary AI frameworks
• Expertise in areas such as few-shot learning, meta-learning, explainable AI
• Experience with AI ethics, responsible AI practices, and navigating regulatory landscapes for AI deployment in the life science industry