Senior Principal Full Stack Engineer

Job description

Senior Principal Full Stack Engineer GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. R&D at GSK is highly data-driven, and we are applying AI/ML, modern software engineering, and data platforms to generate new insights, enable analytics, drive automation, and accelerate the pace of discovery and development. This role is in R&D Technology where you will architect and build production-grade applications and data platforms used by scientists, clinicians, and business stakeholders worldwide. You will work across diverse domains and partner with architects, data engineers, AI/ML modellers, and product owners to deliver high-quality, scalable systems in alignment with agile and DevOps principles. The Role We are seeking a Senior Principal Full Stack Engineer with deep expertise across software development, data engineering, cloud architecture, and AI/ML integration. This is a hands-on technical role where you will spend the majority of your time writing production code, architecting cloud-native solutions, integrating AI capabilities, and driving engineering excellence across the team. At this level, you are expected to own technical direction, make sound architectural decisions, and actively elevate the engineers around you — not just deliver your own work. You bring strong opinions, hold yourself and others to a high engineering bar, and are excited by the challenge of building systems that work reliably at scale. In This Role You Will You will work across a range of the following areas: Software Engineering & Application Development Write clean, well-tested, production-grade code for full-stack applications using Python and modern frontend frameworks Build and maintain scalable REST APIs, microservices, and async processing pipelines Design application architectures and own technical solutions end-to-end Lead and participate in code reviews, enforce quality standards, and drive testing culture Debug and optimise application performance across the full stack AI & GenAI Integration Integrate large language models into production applications via secure, governed API infrastructure Design and build RAG pipelines — document ingestion, chunking, vectorisation, retrieval, and reranking Implement semantic search using vector databases and cloud search services Apply prompt engineering and structured output techniques for reliable, deterministic LLM outputs Build and evaluate agentic workflows including tool calling, multi-step orchestration, and human-in-the-loop patterns Implement LLM observability — latency tracking, cost monitoring, output quality evaluation, and regression testing for prompts Apply AI security practices: prompt injection defence, PII handling, data residency, and output validation Collaborate with data scientists to productionise ML models and evaluate emerging AI frameworks Cloud Architecture & Services Design and architect cloud-native applications and data solutions on Azure Implement scalable, resilient, and cost-effective cloud architectures with a focus on high availability and security Apply cloud security best practices: identity management, RBAC, secrets management, network isolation Implement observability across services — distributed tracing, APM, logging, and alerting Optimise cloud resource utilisation and apply FinOps principles Data Engineering Build and maintain data pipelines for large-scale structured and unstructured data processing Implement ETL/ELT processes across diverse data sources with reliability and observability Design data models and schemas for both analytical and operational workloads Work with cloud data warehouses and distributed processing platforms for analytics and AI/ML data flows Implement data quality checks, monitoring, and governance practices Database & Data Management Write complex SQL queries for data analysis and application needs Design and optimise schemas for relational and NoSQL databases Tune query performance and implement indexing strategies at scale Implement data access patterns, ORM frameworks, and caching strategies DevOps & Infrastructure Implement Infrastructure as Code and mature CI/CD pipelines Containerise applications and manage orchestrated deployments with Docker and Kubernetes Implement monitoring, distributed tracing, logging, and alerting as first-class concerns Automate deployment and operational processes and champion GitOps practices Technical Leadership & Collaboration Drive architectural decisions and set engineering standards across the team Mentor and develop junior and mid-level engineers through code reviews, pairing, and knowledge sharing Represent engineering in cross-functional discussions with product owners, architects, and business stakeholders Proactively identify technical debt, performance bottlenecks, and systemic risks and drive remediation Evaluate and recommend new technologies, frameworks, and engineering practices Minimum Qualifications & Skills Bachelor's degree in Computer Science or equivalent industry experience 15+ years of hands-on software development with clear progression in technical complexity and leadership Expert-level Python programming with extensive production application development experience Strong full-stack development experience across backend frameworks (e.g. FastAPI, Flask, Django) and modern frontend (e.g. React, TypeScript) Demonstrated experience delivering AI/ML features in production — not just prototyping or notebook experimentation Solid understanding of RAG architectures, vector databases, and LLM integration patterns Hands-on experience with prompt engineering, structured outputs, and LLM output validation Cloud platform experience, preferably Azure — managed services, containerised deployments, and observability Strong SQL skills: complex queries, data modelling, and performance optimisation Data engineering fundamentals: building and operating data pipelines at scale Experience building production-grade systems: scalable, maintainable, well-tested, and observable Strong software architecture knowledge: design patterns, microservices, distributed systems, cloud-native design Proven technical leadership: driving standards, mentoring engineers, and owning architectural decisions DevOps practices: CI/CD, containerisation, Infrastructure as Code, and GitOps Excellent problem-solving, communication, and stakeholder engagement skills Essential Skills Azure cloud platform expertise: deep knowledge of managed compute, storage, search, data, and orchestration services Cloud data warehouse and distributed processing experience: e.g. Snowflake, Databricks, Apache Spark — including data governance and Unity Catalog-style patterns Agentic AI experience: tool calling, multi-agent orchestration, LangGraph or equivalent frameworks LLM observability and evaluation: prompt regression testing, latency/cost tracking, output quality monitoring GenAI platform experience: working with leading commercial LLMs via API in production, including gateway-based access patterns Advanced RAG patterns: hybrid retrieval, reranking, multi-modal inputs, context window optimisation DevOps maturity: Infrastructure as Code, advanced CI/CD, GitOps, and cloud security controls Containerisation and orchestration: Docker and Kubernetes at scale Database expertise: PostgreSQL and/or cloud-native relational databases with performance tuning experience Micro-frontend architecture: component-driven, independently deployable frontend modules AI security: prompt injection defence, PII handling in LLM pipelines, data residency controls Preferred Qualifications Azure certifications (Solutions Architect, Developer, or Data Engineer) MLOps knowledge: model deployment, versioning, monitoring, and A/B testing Experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face Knowledge of NLP techniques beyond