<b>Senior Principal Full Stack Engineer</b><div>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.</div>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.<div><b>The Role</b></div>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.<div><b>In This Role You Will</b></div>You will work across a range of the following areas:<b>Software Engineering & Application Development</b>Write clean, well-tested, production-grade code for full-stack applications using Python and modern frontend frameworksBuild and maintain scalable REST APIs, microservices, and async processing pipelinesDesign application architectures and own technical solutions end-to-endLead and participate in code reviews, enforce quality standards, and drive testing cultureDebug and optimise application performance across the full stack<b>AI & GenAI Integration</b>Integrate large language models into production applications via secure, governed API infrastructureDesign and build RAG pipelines — document ingestion, chunking, vectorisation, retrieval, and rerankingImplement semantic search using vector databases and cloud search servicesApply prompt engineering and structured output techniques for reliable, deterministic LLM outputsBuild and evaluate agentic workflows including tool calling, multi-step orchestration, and human-in-the-loop patternsImplement LLM observability — latency tracking, cost monitoring, output quality evaluation, and regression testing for promptsApply AI security practices: prompt injection defence, PII handling, data residency, and output validationCollaborate with data scientists to productionise ML models and evaluate emerging AI frameworks<b>Cloud Architecture & Services</b>Design and architect cloud-native applications and data solutions on AzureImplement scalable, resilient, and cost-effective cloud architectures with a focus on high availability and securityApply cloud security best practices: identity management, RBAC, secrets management, network isolationImplement observability across services — distributed tracing, APM, logging, and alertingOptimise cloud resource utilisation and apply FinOps principles<b>Data Engineering</b>Build and maintain data pipelines for large-scale structured and unstructured data processingImplement ETL/ELT processes across diverse data sources with reliability and observabilityDesign data models and schemas for both analytical and operational workloadsWork with cloud data warehouses and distributed processing platforms for analytics and AI/ML data flowsImplement data quality checks, monitoring, and governance practices<b>Database & Data Management</b>Write complex SQL queries for data analysis and application needsDesign and optimise schemas for relational and NoSQL databasesTune query performance and implement indexing strategies at scaleImplement data access patterns, ORM frameworks, and caching strategies<b>DevOps & Infrastructure</b>Implement Infrastructure as Code and mature CI/CD pipelinesContainerise applications and manage orchestrated deployments with Docker and KubernetesImplement monitoring, distributed tracing, logging, and alerting as first-class concernsAutomate deployment and operational processes and champion GitOps practices<b>Technical Leadership & Collaboration</b>Drive architectural decisions and set engineering standards across the teamMentor and develop junior and mid-level engineers through code reviews, pairing, and knowledge sharingRepresent engineering in cross-functional discussions with product owners, architects, and business stakeholdersProactively identify technical debt, performance bottlenecks, and systemic risks and drive remediationEvaluate and recommend new technologies, frameworks, and engineering practices<div><b>Minimum Qualifications & Skills </b></div>Bachelor's degree in Computer Science or equivalent industry experience<b>15+ years of hands-on software development with clear progression in technical complexity and leadership</b><b>Expert-level Python programming with extensive production application development experience</b><b>Strong full-stack development experience across backend frameworks (e.g. FastAPI, Flask, Django) and modern frontend (e.g. React, TypeScript)</b>Demonstrated experience delivering AI/ML features in production — not just prototyping or notebook experimentation<b>Solid understanding of RAG architectures, vector databases, and LLM integration patterns</b>Hands-on experience with prompt engineering, structured outputs, and LLM output validation<b>Cloud platform experience, preferably Azure — managed services, containerised deployments, and observability</b>Strong SQL skills: complex queries, data modelling, and performance optimisation<b>Data engineering fundamentals: building and operating data pipelines at scale</b>Experience building production-grade systems: scalable, maintainable, well-tested, and observableStrong software architecture knowledge: design patterns, microservices, distributed systems, cloud-native designProven technical leadership: driving standards, mentoring engineers, and owning architectural decisionsDevOps practices: CI/CD, containerisation, Infrastructure as Code, and GitOpsExcellent problem-solving, communication, and stakeholder engagement skills<div><b>Essential Skills </b></div><b>Azure cloud platform expertise: </b>deep knowledge of managed compute, storage, search, data, and orchestration services<b>Cloud data warehouse and distributed processing experience: </b>e.g. Snowflake, Databricks, Apache Spark — including data governance and Unity Catalog-style patterns<b>Agentic AI experience: </b>tool calling, multi-agent orchestration, LangGraph or equivalent frameworks<b>LLM observability and evaluation: </b>prompt regression testing, latency/cost tracking, output quality monitoring<b>GenAI platform experience: </b>working with leading commercial LLMs via API in production, including gateway-based access patterns<b>Advanced RAG patterns: </b>hybrid retrieval, reranking, multi-modal inputs, context window optimisation<b>DevOps maturity: </b>Infrastructure as Code, advanced CI/CD, GitOps, and cloud security controls<b>Containerisation and orchestration: </b>Docker and Kubernetes at scale<b>Database expertise: </b>PostgreSQL and/or cloud-native relational databases with performance tuning experience<b>Micro-frontend architecture: </b>component-driven, independently deployable frontend modules<b>AI security: </b>prompt injection defence, PII handling in LLM pipelines, data residency controls<div><b>Preferred Qualifications</b></div>Azure certifications (Solutions Architect, Developer, or Data Engineer)MLOps knowledge: model deployment, versioning, monitoring, and A/B testingExperience with ML frameworks such as PyTorch, TensorFlow, or Hugging FaceKnowledge of NLP techniques beyond basic text processing — entity extraction, classification, embeddingsExperience with cloud search and indexing technologiesFinOps practices: cloud cost attribution, optimisation, and governanceExperience in pharmaceutical, healthcare, or regulated industry environmentsSecure coding practices and software security fundamentalsExperience with data visualisation libraries for analytical dashboardsFamiliarity with AI-assisted development tools and practices<br /><b>Skills</b>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 <b>Why GSK?</b><b>Uniting science, technology and talent to get ahead of disease together.</b>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.<b>Inclusion at GSK:</b>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 <a target="_blank" href="http://IN.recruitment-adjustments@gsk.com"><span style="color:#0000ff"><u>IN.recruitment-adjustments@gsk.com</u></span></a> to discuss your needs.<b>Important notice to Employment businesses/ Agencies</b>GSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.It has come to our attention that the names of GlaxoSmithKline or GSK or our group companies are being used in connection with bogus job advertisements or through unsolicited emails asking candidates to make some payments for recruitment opportunities and interview. Please be advised that such advertisements and emails are not connected with the GlaxoSmithKline group in any way.<b>GlaxoSmithKline does not charge any fee whatsoever for recruitment process. Please do not make payments to any individuals / entities in connection with recruitment with any GlaxoSmithKline (or GSK) group company at any worldwide location. Even if they claim that the money is refundable.</b>If you come across unsolicited email from email addresses not ending in gsk.com or job advertisements which state that you should contact an email address that does not end in “gsk.com”, you should disregard the same and inform us by emailing <a target="_blank" href="http://askus@gsk.com"><span style="color:#0000ff"><u>askus@gsk.com</u></span></a>, so that we can confirm to you if the job is genuine.
