Senior Principal Full Stack Engineer

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 basic text processing — entity extraction, classification, embeddings

Experience with cloud search and indexing technologies

FinOps practices: cloud cost attribution, optimisation, and governance

Experience in pharmaceutical, healthcare, or regulated industry environments

Secure coding practices and software security fundamentals

Experience with data visualisation libraries for analytical dashboards

Familiarity with AI-assisted development tools and practices

Skills Artificial Intelligence (AI), Artificial Intelligence Ethics, Artificial Neural Networks (ANNS), Classification Models, Deep Learning, Intelligent Automation (IA), Machine Learning (ML), Model Evaluation, Model Validation, Predictive Modeling, Probabilistic Modeling, Python (Programming Language), Test Documentation    

Why GSK? Uniting science, technology and talent to get ahead of disease together.

GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.

People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.

Inclusion at GSK: As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.

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