Provide tailored guidance to business units on AI/ML use cases, feasibility, model selection, and deployment options, particularly in scientific domains without active AI/ML engineering efforts.
Co-design prototypes and proof-of-concepts (PoCs) with product and domain teams to validate ideas quickly and de-risk larger investments.
Translate complex stakeholder requirements into well-scoped technical solutions with clear success criteria and handover plans.
Build, train, evaluate, and iterate on ML models for real-world scientific and business problems—including but not limited to NLP/LLM applications, knowledge graphs, causal inference, computer vision, and predictive modeling.
Package trained models into production-ready services (APIs, containerized deployments) using GSK’s cloud infrastructure (GCP/AWS/Azure).
Develop and maintain agentic AI systems, multi-agent architectures, and LLM-based tools where appropriate.
Share reusable patterns, baseline models, and tested pipelines for common AI/ML tasks.
Embed privacy, ethics, and regulatory considerations into every engagement from the outset.
Run workshops, seminars, and hands-on training sessions to increase AI literacy across the organization.
Embed within business/research units for time-limited engagements (typically 6–8 weeks) to accelerate delivery and transfer skills.
Communicate relevant issues, requests, and opportunities from business units back to AI/ML product leads.

Bachelor’s degree in computer science, Machine Learning, Computational Biology, Bioinformatics, Statistics, Engineering, or a related quantitative discipline; OR equivalent professional experience as a software/ML engineer.
2+ years of professional experience developing and deploying machine learning models (with a Bachelor’s); 1+ years with a Master’s or PhD.
Expertise in Python, including ML/data science libraries (PyTorch, TensorFlow, JAX, scikit-learn, pandas, numpy).
Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
Experience working in healthcare, pharma, or biological domains.

Applied AI Engineer

Key skills