Lead AI Engineer, Biomedical & Vigilance Innovation

About the role

We are seeking a Lead AI Engineer, Biomedical & Vigilance Innovation with a start-up mindset to drive the design, development, and deployment of advanced AI solutions.

Responsibilities

• Design, build, validate, and maintain machine learning, natural language processing, and generative AI solutions for biomedical and pharmacovigilance use cases.
• Engineer predictive models to identify emerging risks, patient patterns, and operational bottlenecks.
• Translate complex scientific and business requirements into production-ready AI applications.
• Define and execute a bold technology strategy spanning the Global Patient Safety covering usage of AI in day-to-day PV operations, analytical sciences, signal detection, process scale up with a clear mandate to embed AI, machine learning, and agentic automation throughout, within the UT Global Patient Safety Organization.
• Drive the architecture, development, and delivery of next-generation platforms for pharmacovigilance technological AI initiatives.
• Integrate structured and unstructured datasets from safety databases, clinical systems, literature sources, real-world evidence, and external repositories.
• Create scalable pipelines for ingestion, transformation, and quality control of biomedical data.
• Apply ontology mapping, terminology harmonization, and metadata strategies across MedDRA, WHO Drug, and related standards.
• Ensure robust data lineage, traceability, and audit readiness.
• Support modernization of pharmacovigilance and Organovigilance systems through AI enabled automation and decision support tools.
• Improve case processing efficiency, medical review, and governance reporting through AI-enabled solutions.
• Develop AI enabled dashboards and visualization tools that enable rapid interpretation of safety trends.
• Ensure AI models and digital tools are developed in alignment with GxP, privacy, security, validation, and regulatory expectations.
• Maintain documentation for validation, testing, intended use, and lifecycle management.
• Collaborate with other Safety Functions, Clinical Operations, Regulatory Affairs, Medical Affairs, Biostatistics, and IT functions.
• Provide technical guidance to analysts, data scientists, and business partners.
• Deliver validated AI solutions that create measurable gains in vigilance quality, speed, and insight generation.
• Improve detection and prioritization of safety signals through advanced analytics.
• Enhance case processing and review efficiency while preserving quality and compliance.
• Maintain regulatory-ready governance for AI-enabled safety systems.
• Advance UTC’s leadership position in responsible AI for the future of medicine.

Qualifications

• Bachelor’s Degree in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 8+ years of relevant experience.
• Master’s Degree in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 6+ years of relevant experience.
• Doctor of Philosophy (PhD) in computer science, science, engineering, applied mathematics, data science, biomedical engineering, bioinformatics, artificial intelligence, or related discipline with 2+ years of relevant experience.
• 5+ years of experience in AI engineering, machine learning, or advanced analytics within biopharma, healthcare, or regulated industries.
• 5+ years of hands-on expertise in AI tools (e.g. Python/R/Matlab for ML, TensorFlow/PyTorch, cloud-based ML platforms).
• Track record of applying AI, machine learning, and data science to solve hard problems in complex domains not just strategy but working implementations that have delivered measurable outcomes in production environments.
• Entrepreneurial, transformation-oriented mindset with the ability to move from concept to execution quickly, sustain momentum through ambiguity, and lead organizations through technology-driven change.
• Background in AI-native product development including agentic AI, LLM-powered applications, autonomous systems, computer vision, or ML-driven process optimization with hands-on experience developing & implementing AI products, not just evaluating them.
• Strong problem-solving capability with the ability to operate in complex matrixed environments.
• Deep fluency in cloud-native engineering, platform architecture, and modern software development practices.
• Communicate technical outputs clearly to non-technical stakeholders and senior leadership.
• Learn the science quickly and develop enough depth to challenge assumptions, ask the right questions, and identify where technology can create step-change improvements in manufacturing timelines, quality, and cost.

Skills

• Strong understanding of machine learning, natural language processing, and generative AI.
• Experience with cloud-based ML platforms.
• Hands-on experience with AI tools (e.g. Python/R/Matlab for ML, TensorFlow/PyTorch).
• Experience with NLP, LLMs, knowledge graphs, or biomedical text mining.
• Familiarity with data architecture, cloud computing, and innovative analytics platforms.
• Experience with safety systems such as Argus, ArisG, Veeva, or equivalent platforms.
• Knowledge of pharmacovigilance, clinical development, biomedical data, or healthcare regulations.
• Experience with PK/PD modeling and simulation.
• Familiarity with GVP, FDA, EMA, ICH, and data privacy frameworks.

Benefits

• Comprehensive benefits suite of programs, including medical / dental / vision / prescription coverage, employee wellness resources, savings plans (401k and ESPP), paid time off & paid parental leave benefits, disability benefits, and more.

Pay

Commensurate with experience.

Schedule

Hybrid schedule of 4 days in office and the option to work 1 day each week from home. In office requirements could change based on business needs.