Principal AI Engineer - Evinova

AstraZeneca•Gaithersburg, MD

8d•$144,649 - $189,852•Hybrid

About The Position

Evinova is a health-tech business focused on advancing digital transformation across the life sciences sector and improving health outcomes through digital solutions. The Principal AI Engineer will design and implement sophisticated agentic AI systems for life sciences, building intelligent agents that reason, plan, and act autonomously to address complex clinical challenges. The role combines AI research with practical healthcare applications and involves work on solutions for clinical trials, drug discovery, and patient care. The engineer will develop LLM-based agents, multi-agent systems, NLP systems, and methods for agent memory, tool use, evaluation, prompt optimization, and performance monitoring. The role includes collaboration with product, design, clinical, and machine learning teams; mentoring engineers and contributing to AI strategy; and sharing technical work through publications, conferences, and open-source contributions. Example projects include clinical document generation, medical research search, clinical trial optimization, synthetic patient data generation, and multimodal healthcare assistants. Employees are expected to work from the office three days per week. The annual base pay range is $144,648.80–$189,851.55 USD, with a short-term incentive bonus opportunity and eligibility to participate in an equity-based long-term incentive program.

Requirements

Bachelor's degree in Engineering, Mathematics, or Computer Science.
At least 4 years of applied machine learning experience, with a strong focus on deep learning, NLP, and generative AI.
Extensive experience exploring and testing language-model behavior, prompting, and building products with language models.
Expert knowledge of Python and advanced ML/LLM frameworks, such as TensorFlow, PyTorch, Google ADK, CrewAI, LangChain, or LlamaIndex.
Extensive experience with AWS services, such as SageMaker, Bedrock, MSK, EKS, ECS, or OpenSearch.
Deep understanding of agentic AI systems and frameworks, including agentic design patterns, multi-agent systems, and reinforcement learning.
Excellent communication skills and the ability to explain complex technical concepts to technical and non-technical audiences.

Nice To Haves

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

Responsibilities

Build and deploy sophisticated agentic AI solutions using state-of-the-art large language models.
Develop approaches to agent memory, tool use, and multi-agent collaboration.
Build NLP systems for retrieval, information extraction, structured generation, and graph reasoning.
Create automated techniques for agent design, evaluation, and optimization.
Discover and validate prompt-engineering approaches for agentic systems.
Build observability pipelines for continuous model and agent performance monitoring.
Partner with product, design, and clinical teams to translate AI capabilities into healthcare solutions.
Mentor engineers and contribute to AI strategy across the organization.
Share expertise through conferences and technical publications.
Contribute to open-source projects and advance best practices in healthcare AI.