About the Role
Would you like to work closely with scientists to bring AI, automation and data into the lab? Are you an experienced engineer who can get to the heart of a problem quickly, then design and build a solution that works and take it into production? As a Senior Principal Forward Deployed DevSecOps Engineer in Labs & CMC Digital & Tech, you will work directly with our Product Owners, scientists and data engineering teams to shape and deliver digital products for the lab. This is a hands-on engineering role. The work could be orchestrating lab workflows, automating lab processes, or building agentic workflows that take manual and repetitive work away from scientists. Where a solution needs data, you will work with data engineering to land it and then build on top of it. Strong DevSecOps and cloud infrastructure skills are a big part of the job, so that security, automated testing, monitoring and repeatable infrastructure are there from the start and we can release often with confidence. You will own your work from early discovery through to production and beyond, and as one of the more senior engineers in the team you will help others develop too. We care about software that scientists use every day. That means less manual lab work, fewer errors, better data, and medicines and vaccines moving through the pipeline faster.
Why This Role Exists
Labs & CMC is changing how laboratory science and CMC work gets done, using digital technology, data, automation and AI. To make that happen we need senior engineers who can work directly with Product Owners and scientists, understand the science and the GxP context well enough to ask the right questions, and then lead the design and build of a sound solution. This is a broad role. Data is part of it, but so are lab automation, systems integration, agentic workflows that cut down manual effort, and the DevSecOps and cloud infrastructure that keeps it all running.
• Shaping the solution: working with Product Owners and scientists to understand the problem, pairing with them on the options, and driving towards a solution that is technically sound and fits how the lab works.
• Moving quickly: designing and building PoCs, PoVs and MVPs on real lab data in days or weeks, and giving a clear view on whether each one should go further.
• Automating the lab: connecting instruments, lab systems, inventory and data platforms, and building automated and agentic workflows that reduce manual activity for scientists.
• Landing the data: working with data engineering teams to get the right data in place and in the right shape, so solutions, analytics and machine learning can be built on it.
• Getting to production safely and often: taking proven ideas through to secure, supportable and compliant products, with infrastructure as code, CI/CD, automated testing and monitoring built in so changes can be released quickly and with confidence.
• Building for reuse: leaving behind orchestration patterns, agents and components that other teams across Labs & CMC can use.
Key Responsibilities
Discovery, Solution Design and Prototyping
• Work directly with Product Owners, scientists and data engineering teams to understand lab workflows, the data involved, the constraints and what a good outcome looks like.
• Pair with Product Owners to shape both the problem and the solution, challenge requirements where needed, and recommend the right technical approach.
• Design and build PoCs, PoVs and MVPs in short cycles using real lab data and systems, with regular feedback from scientists.
• Look at feasibility, usability, data quality and compliance early. Estimate the benefit in time saved, errors avoided and manual effort removed, and recommend whether to continue, change direction or stop.
Engineering and Delivery
• Write, test and ship code, and own the technical design of the products you work on.
• Lead the move from MVP to production, covering architecture, integration, testing, security, deployment, monitoring, documentation and support handover. Where the route is clear, ship it yourself.
• Keep technical debt under control and hand over cleanly to product and platform teams with working code, design notes and known risks.
DevSecOps, Quality and Observability
• Set up and maintain a streamlined SDLC for your products, with CI/CD pipelines that let the team release small changes often and safely.
• Build automated testing into every pipeline, including unit, integration, end-to-end and performance tests, so quality checks run on every change and not just before a release.
• Build security in from the start through secure coding, dependency and container scanning, static and dynamic code analysis, and secrets management. Fix vulnerabilities promptly in line with GSK cyber security standards.
• Make sure solutions meet requirements for privacy, data integrity, and GxP and computer system validation where they apply. Where possible, use the pipeline to generate test evidence and audit trails automatically.
• Design observability in from day one, with logging, metrics, tracing, dashboards and alerts using tools such as Grafana, Prometheus and Elastic.
• Integrate products with ServiceNow for incident, change and service management so they are ready for support and operational handover.
• Track delivery and service measures such as deployment frequency, lead time, change failure rate and time to restore, and use them to keep improving how the team works.
Cloud Infrastructure
• Work with the GSK central Digital Platforms team to get the cloud infrastructure your products need, for example on Azure or GCP. Be clear about what is needed, review designs, and make sure it fits the solution.
• Use your knowledge of cloud networking, identity, access and security to design solutions that run well on that infrastructure, and to work through issues when they come up.
• Where needed, provision and configure infrastructure yourself using infrastructure as code tools such as Terraform, following central platform standards and patterns.
• Package and deploy applications and services using container platforms and tools such as Kubernetes and Helm, with sensible settings for scaling, resilience and cost.
• Manage development, test and production environments through the pipeline rather than by hand, and keep them consistent with each other.
Labs & CMC Solutions
• Lab Workflow Orchestration: build and extend our lab orchestration platform, which links lab, data and generative design systems, so scientists can run and monitor lab workflows from one interface.
• Lab Automation: design and build automation that connects instruments, lab execution and data capture, moving towards closed-loop experiments where results feed straight into the next experiment design.
• Agentic Workflows: build AI agents and agentic workflows that take on manual, repetitive tasks for scientists and CMC teams, such as gathering data, checking results and preparing documents.
• Landing the Data: work with data engineering teams to bring lab, analytical, CMC and inventory data into well-structured data products, and build solutions on top of them that support analytics, traceability and machine learning.
• Scientific Decision Support: use GenAI and machine learning to help scientists plan experiments, decide on next steps and find relevant scientific knowledge.
AI-Native Engineering
• Use approved AI coding assistants and agent-based workflows for planning, coding, testing, review and documentation as your normal way of working, while checking and correcting what they produce.
• Build and use the right harnesses for AI coding agents, such as reusable skills, project context and instructions, tool and MCP connections, and test and review loops, so agents produce reliable code with less rework. Share what works with the rest of the team.
• Design and build agent-based systems, retrieval-augmented generation, MCP integrations and evaluation frameworks. Decide how much to automate and where scientists need to stay in control.
• Put human review steps, evaluation tests, guardrails and monitoring in place so AI features stay accurate, explainable and suitable for scientific and GxP use.
Technical Leadership, Governance and Coaching
• Raise Responsible AI, data integrity and validation questions at the prototype stage and work with Quality, Cyber and Risk colleagues to resolve them.
• Add useful agents, orchestration templates, integration patterns and reference designs to the Labs & CMC pattern library after each piece of work.
• Mentor and coach other engineers alongside your own delivery work.
• Use what you learn from working with the business to inform roadmaps, PI Planning and portfolio priorities.
What You Bring
Required Experience
• At least 8 years of full-stack, software, data or AI/ML engineering experience, including production systems where you were the technical lead or owner.
• A track record of designing and building working software quickly and then taking it into production. Experience in a startup, innovation team or forward deployed role is a plus.
• Strong Python skills and at least one of TypeScript/JavaScript, Go, Java or similar, along with solid API, integration and cloud engineering experience.
• Hands-on experience with GenAI, including agents, agentic and multi-agent workflows, retrieval-augmented generation, prompt engineering and evaluation.
• Experience with workflow orchestration and automation tools (for example n8n, Airflow or Temporal) and with working alongside data engineering teams on data pipelines and data products.
• Strong DevSecOps experience, including building CI/CD pipelines (for example GitHub Actions, Azure DevOps or Jenkins), automated unit, integration and performance testing, and security scanning built into the pipeline.
• Solid cloud infrastructure experience on at least one major cloud platform (for example Azure, GCP or AWS), including networking, identity and security, provisioning infrastructure using infrastructure as code (for example Terraform), deploying with container tools (for example Kubernetes or Helm), and working with central platform teams.
• Experience setting up observability with tools such as Grafana, Prometheus and Elastic, and working with ServiceNow for incident and change management.
• Regular use of AI coding tools such as Claude Code or GitHub Copilot in your day-to-day development.
• Experience working directly with Product Owners and business users to shape requirements and drive a solution, and explaining options and risks clearly to senior stakeholders.
• Experience working with teams across different locations and time zones.
• Experience in a regulated environment, with a practical understanding of security, privacy, quality and compliance.
Preferred Experience
• Experience in pharma, biotech or life sciences R&D.
• Experience with instrument integration, robotics, or automated and closed-loop experimentation.
• Knowledge of quality and regulatory requirements for software in a regulated industry, for example GxP, computer system validation and data integrity.
• Experience working in a hybrid cloud environment.
• Previous experience as a Forward Deployed Engineer, Customer Engineer or Solutions Architect.
• Experience sharing reusable engineering patterns with a wider team and mentoring other engineers.
Education
Bachelor’s degree in Computer Science, Software Engineering, Data Science, Engineering or a related subject, or relevant industry experience.
#AGTech #GSK-LI
Skills Agile Methodology, API Management, CI/CD, Cloud Based Services, Computer Programming, Configuration Management (CM), Containerization, Database Management, High Availability (Inactive), Infrastructure As Code (IaC), Microservices Architecture, Network Security, Operating Systems (OS), System Monitors, Version Control System
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