About the Role
We're after a hands-on engineer for our Forward Deployed team in Labs & CMC Digital & Tech, building the automation, data pipelines and AI tooling that take manual work out of the lab. You'll typically work closely alongside a Senior Principal Forward Deployed DevSecOps Engineer on the approach, coordinating with stakeholders as needed, while your own focus stays on turning that direction into working software: quickly, securely, and in a way that still holds up once it's live. As you build experience, you may take on more of that stakeholder relationship yourself, with support and guidance from leadership. Expect a lot of coding, pipeline work and cloud infrastructure, with security and testing built in as you go rather than bolted on at the end. Most weeks you'll be deep in one piece of the build: automating a lab workflow, wiring up a data pipeline, or getting an agentic prototype into decent shape, while staying close enough to the rest of the engagement to flag problems before they land on someone else's desk. As you get more experience, you'll be trusted with a bigger slice of the problem yourself, and eventually the whole thing. We care about software that scientists actually use. Less manual lab work, fewer errors, better data, medicines and vaccines moving faster through the pipeline.
Why This Role Exists Labs & CMC is rebuilding how laboratory science and CMC work gets done, with automation, data and AI doing jobs scientists used to do by hand. Senior engineers typically lead and coordinate the relationship with the business side of that, working in close partnership with the wider team; this role exists to give them the extra hands to actually build what's agreed, while staying closely involved in how the work takes shape. You'll spend most of your time writing code, wiring up pipelines and getting things into production, picking up more ownership of the problem, and potentially of the stakeholder relationship itself, as you grow into the role and build a deeper understanding of what's needed.
• Turning direction into software: working with a Senior Forward Deployed DevSecOps Engineer to agree an approach, coordinating with stakeholders as needed, then building it properly rather than knocking together a demo.
• Keeping pace: most of what you'll build needs to land in days, not sprints, so you'll want to be comfortable shipping fast and adjusting as scientist feedback comes in.
• Automation that actually sticks: wiring up instruments, lab systems and data platforms so what you build is still running after the project wraps up.
• Getting the data right: pairing with data engineering so whatever you're building sits on data that's actually trustworthy.
• Production-ready by default: security, testing and monitoring go in as you build, not after something's already gone wrong.
• Leaving something behind: writing components and patterns that the next engineer, on this engagement or another one, can actually pick up and use.
Key Responsibilities:
Working the Problem
• Partner with Product Owners, Product Managers, Business Owners and scientists to understand laboratory and CMC workflows first-hand, clarify the required outcome and identify delivery, data, security or compliance risks early.
• Translate the agreed approach into a realistic technical plan, build rapid prototypes using representative laboratory data and incorporate scientist feedback before progressing the solution.
Building and Shipping
• Design, code and test software, integrations, automation and data pipelines, taking ownership of day-to-day technical decisions within the agreed architecture.
• Develop successful prototypes into secure, reliable and supportable production solutions, confirming significant architectural decisions with a Senior Principal Forward Deployed DevSecOps Engineer or the relevant technical leader.
• Coordinate dependencies with internal, vendor and contract engineers, communicate delivery risks promptly and provide clear code, documentation and handover materials.
Keeping It Secure and Reliable
• Build continuous integration and continuous delivery pipelines that enable frequent, controlled and traceable releases.
• Implement unit, integration and end-to-end testing, together with dependency scanning, secrets management and least-privilege access.
• Apply relevant GxP, data-integrity, privacy and security requirements, documenting technical decisions and obtaining specialist guidance where needed.
• Establish production observability using appropriate logging, metrics and alerting tools, and connect solutions with ServiceNow processes to support operational readiness and incident management.
Cloud Infrastructure
• Provision approved Azure or Google Cloud Platform infrastructure through the central Digital Platforms team, following established architecture, networking, identity, security and cost-management guardrails.
• Define infrastructure using Terraform and deploy applications using Kubernetes or Helm, applying appropriate settings for resilience, scaling and cost.
• Maintain consistency across development, test and production environments by managing configuration and infrastructure changes through automated pipelines.
Contributing to Labs & CMC Solutions
• Build and extend workflows and integrations within laboratory orchestration platforms, enabling scientists to manage and track work through connected systems.
• Integrate laboratory instruments and systems to automate data capture and reduce manual entry.
• Develop AI agents and workflow automation that reduce repetitive scientific and operational tasks.
• Partner with data engineers to make laboratory, analytical and inventory data accessible and trustworthy for the solutions being developed.
• Build Generative AI capabilities that support activities such as experiment planning, information retrieval and scientific decision-making.
Building With AI
• Use approved AI-assisted software-development tools and agent workflows, reviewing, testing and validating generated outputs before use.
• Reuse existing skills, prompts, components and Model Context Protocol connections, and contribute identified capability gaps to the wider team.
• Build AI agents, retrieval-augmented generation pipelines and Model Context Protocol integrations, seeking technical guidance for complex evaluation or data-access decisions.
• Apply appropriate guardrails, evaluation, traceability and human review to AI-enabled capabilities, particularly where scientific, regulated or GxP-relevant information is involved.
Working Well With Others
• Escalate Responsible AI, data-integrity, security or validation concerns as soon as they are identified.
• Contribute reusable components, agents and engineering patterns to the Labs & CMC library, supported by documentation that enables adoption by other engineers.
• Support internal, vendor and contract engineering colleagues with technical problem-solving and knowledge sharing.
• Share delivery lessons, recurring risks and improvement opportunities with technical leaders so that they can inform broader engineering decisions.
What You Bring
Required Experience:
• 5+ years building software professionally, across full-stack, data or AI/ML work, including some time on systems that actually reached production, not just proofs of concept.
• Comfortable taking a reasonably well-defined problem and shipping working software against it quickly.
• Solid Python, plus at least one of TypeScript/JavaScript, Go or similar.
• Hands-on with GenAI patterns, including agents, RAG, prompt engineering and evaluation, not just familiar with the terms.
• Some experience with workflow orchestration tools (n8n, Airflow, Temporal) and working alongside data engineers.
• DevSecOps basics: CI/CD pipelines, automated testing, security scanning built into how you work rather than applied afterwards.
• Cloud experience on Azure or GCP, our preferred platforms, including provisioning with Terraform and deploying with Kubernetes or Helm.
• Comfortable setting up observability with Grafana, Prometheus or Elastic, and familiar with how ServiceNow fits into incident and change processes.
• Use AI coding tools such as Claude Code or GitHub Copilot daily, not occasionally.
• Comfortable working closely with a more senior engineer on the shape of a problem, while owning how you build the solution yourself, and ready to take on more of that framing role as you gain experience.
• Comfortable working across time zones with a distributed team.
• Some exposure to regulated environments, so security, privacy and quality considerations aren't new territory.
Preferred Experience:
• A background in pharma, biotech or life sciences.
• Instrument integration, robotics or closed-loop experimentation experience.
• Familiarity with GxP, computer system validation or data integrity requirements.
• Hybrid cloud experience.
• A previous stint as a Forward Deployed Engineer, Customer Engineer or Solutions Architect.
• Have worked alongside vendor or contract engineers on a shared piece of delivery.
• Contributed a pattern or component that someone else actually reused.
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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