Principal Forward Deployed DevSecOps Engineer

Overview

A hands-on engineer in GSK's Forward Deployed team in Labs & CMC Digital & Tech, building automation, data pipelines and AI tooling that remove manual work from the lab, working alongside a Senior Principal Forward Deployed DevSecOps Engineer.

What you'll do

• Partner with Product Owners, Product Managers, Business Owners and scientists to understand laboratory and CMC workflows and identify delivery, data, security or compliance risks early.
• Translate the agreed approach into a technical plan, build rapid prototypes using representative laboratory data and incorporate scientist feedback.
• Design, code and test software, integrations, automation and data pipelines, owning 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.
• Build CI/CD 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 logging, metrics and alerting tools, and connect solutions with ServiceNow processes for operational readiness and incident management.
• 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.
• 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.
• Build Generative AI capabilities supporting experiment planning, information retrieval and scientific decision-making.
• 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 guardrails, evaluation, traceability and human review to AI-enabled capabilities, particularly where scientific, regulated or GxP-relevant information is involved.
• 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.
• 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.

What you'll need

• 5+ years building software professionally across full-stack, data or AI/ML work, including time on systems that 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 experience with GenAI patterns, including agents, RAG, prompt engineering and evaluation.
• Some experience with workflow orchestration tools (n8n, Airflow, Temporal) and working alongside data engineers.
• DevSecOps basics: CI/CD pipelines, automated testing, and security scanning built into how you work.
• Cloud experience on Azure or GCP, 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.
• Comfortable working across time zones with a distributed team.
• Some exposure to regulated environments, so security, privacy and quality considerations are familiar.
• Bachelor's degree in Computer Science, Software Engineering, Data Science, Engineering or a related subject, or relevant industry experience.

Nice to have

• 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.
• Experience working alongside vendor or contract engineers on a shared piece of delivery.
• Contributed a pattern or component that someone else actually reused.

Details

• Location: Bengaluru, Luxor North Tower, India.
• Works across time zones with a distributed team.