AI Engineer

What You'll Do Design, develop, and deploy novel AI/ML systems — with a focus on LLMs and computer vision — to recover, extract, and automate clinical data tasks across diverse data types including medical records, imaging, and clinical reports Build robust prototypes and transition them to scalable, near-production-ready code, applying strong software engineering fundamentals throughout Design and execute rigorous statistical validation and evaluation frameworks, defining performance metrics and ensuring models meet stakeholder-defined accuracy thresholds Own the human-in-the-loop (HITL) lifecycle, ensuring deployed models can continuously improve over time through structured feedback capture Partner closely with software engineering to bring proof-of-concept solutions into production, contributing to key architectural decisions along the way Proactively identify high-impact AI opportunities across the business and define how advanced capabilities can address them Champion best practices for documentation, experimentation, and code quality across the model development lifecycle — and potentially mentor others in doing the same Actively evaluate state-of-the-art approaches, bringing innovative ideas and methods to the team Apply and uphold healthcare data privacy regulations and ethical standards in all AI development work Requirements MSc or PhD in computer science, engineering, applied mathematics, statistics, or a related field; BSc with equivalent demonstrated experience also considered 2–5+ years of relevant experience in AI/ML engineering or data science, with increasing scope and responsibility over time Significant experience with at least one deep learning framework (PyTorch, TensorFlow, etc.) applied to LLMs or computer vision in production contexts Proven knowledge of the healthcare data domain in at least one area: EHR/EMR, clinical imaging, clinical workflows, or clinical research Extensive experience with modern cloud compute and storage platforms (AWS, Azure, Databricks, Snowflake, etc.) Solid understanding of database technologies (SQL, NoSQL, etc.) Strong familiarity with SDLC and agile methodologies Ability to independently architect end-to-end solutions and clearly communicate how they deliver business value Excellent written and verbal communication skills — including the ability to present complex technical concepts to non-technical stakeholders Nice to Have Experience with computer vision or multimodal model development Background in clinical research, regulated healthcare environments, or medical data pipelines Familiarity with EDC systems or clinical trial data infrastructure Experience designing or improving HITL feedback systems in production Exposure to AI evaluation frameworks or quality assessment tooling If you\'re interested in shaping how AI systems are built, evaluated, and deployed in high-trust environments, this is an opportunity to have direct influence on both technical direction and real-world impact at a fast-growing company. #J-18808-Ljbffr Harnham