Senior Applied AI Engineer

About the Role

At GSK, we unite science, technology and talent to get ahead of disease together. Our ambition is to positively impact the health of 2.5 billion people over the next decade. We are building a future where state‑of‑the‑art software, AI, and machine learning enable us to discover new therapies and personalized medicines that drive better outcomes for patients—at reduced cost and with fewer side effects. The Applied AI team sits at the intersection of business need and technical capability within the AI/ML department. We directly support business units with AI/ML‑related challenges, acting as ambassadors for responsible AI across the organization. This role is your opportunity to work at the frontier of applied machine learning in one of the world’s leading biopharma companies, translating cutting‑edge AI research into real scientific and business impact.

Key Responsibilities

• Advisory & Solution Design: 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.
• Model Development & Deployment: 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, containerised 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.
• Knowledge Transfer & Enablement: 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.

Why you?

Basic Qualifications:

• 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.
• 3+ years of professional experience developing and deploying machine learning models (with a Bachelor’s); 2+ 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 containerisation (Docker, Kubernetes).
• Strong understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, feature engineering, and experiment tracking.
• Experience working in cross‑functional teams and communicating technical concepts to non‑technical stakeholders.
• Experience working in healthcare, pharma, or biological domains.

Preferred Qualifications:

• Experience in pharma, biotech, or life sciences—particularly in drug discovery, genomics, clinical data, or biological data analysis.
• Hands‑on experience building LLM‑based applications, agentic AI systems, RAG pipelines, or multi‑agent architectures (e.g., LangChain, LangGraph, AutoGen).
• Experience with knowledge graph construction, causal inference, or large perturbation models.
• Familiarity with single‑cell RNA‑seq, spatial transcriptomics, CRISPR assay data, or other high‑dimensional biological datasets.
• Experience with MLOps practices: CI/CD for ML, model monitoring, experiment tracking (MLflow, Weights & Biases), and reproducible research workflows.
• Contributions to open‑source ML/AI projects or peer‑reviewed publications in applied ML.
• Background or demonstrated interest in responsible AI, AI ethics, or model governance.
• Strong software engineering practices: version control (Git/GitHub), code review, testing, and documentation.
• Experience evaluating and integrating third‑party AI/ML vendor tools and platforms.