# Senior Director, Distinguished Engineer – SDLC and Autonomous Development, DevCMO Tech at GSK

GSK is hiring a Senior Director, Distinguished Engineer – SDLC and Autonomous Development, DevCMO Tech for a remote role open to applicants in India. Level rates it Works on AI ●●●○; you can [apply on GSK](https://jobsbylevel.com/go/f55e22b9-d596-4058-9786-10446ebd87da?utm_medium=ai-agent).

AI Level 3, AI centrality 66 out of 100. Remote (Bengaluru Luxor North Tower).

## Details

- Company: [GSK](https://jobsbylevel.com/companies/gsk)
- AI level: AI Level 3 (score 66 out of 100)
- Location: Remote (Bengaluru Luxor North Tower)
- Posted: November 11, 2026
- Apply: https://jobsbylevel.com/go/f55e22b9-d596-4058-9786-10446ebd87da?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-director-distinguished-engineer-sdlc-and-autonomous-development-devcmo-tech-at-gsk-6ca229

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## Description

Are you a recognized leader in modern software engineering, with the technical depth and the credibility to change how a large engineering organization builds software? Do you want to lead the move to autonomous, AI-driven development at GSK, and prove its value with evidence? Do you want to apply this to Clinical Trials technology, to deliver faster, reduce risk and optimize cost and timelines? We'd like to hear from you if you think you might make a good fit! Job Purpose This is a senior leadership and technical authority role for the evolution of the Software Development Life Cycle (SDLC) across DevCMO Tech engineering. You will set the direction for, and lead the adoption of, autonomous development tools such as Cursor Cloud Agents and Cognition, or comparable platforms, so that engineering teams can make their development process progressively more autonomous. This supports strategic programs to improve patient enrolment in clinical trials and accelerate regulatory submissions using AI and modern tooling You will do this by working inside engineering teams and across organizational boundaries. You will be hands-on with the tools and the Azure and GCP infrastructure behind them, and you will gather the evidence that shows what they deliver. You will lead a small team and work closely with other architects, engineers, product teams, R&D Tech central services, and Security, Quality and Regulatory partners. Key Responsibilities: Technical direction and tooling Set the technical strategy for autonomous development across DevCMO Tech, and evaluate tools such as Cursor Cloud Agents, Cognition or similar through hands-on proofs of concept. Recommend what to adopt based on value, security, compliance and cost. Lead the implementation of approved tools, including environments, access and permissions, guardrails, and cost and usage monitoring. Integrate autonomous agents into collaboration and delivery tooling, including Microsoft Teams and Azure DevOps (work items, repos, pull requests, pipelines and approvals), so agents can take work from backlog to reviewed code with appropriate human oversight. Architect and oversee the Azure and GCP cloud infrastructure that supports these tools, including identity, networking, secrets management, sandboxed execution environments and observability. Keep autonomous development practices compliant with GSK Security, Quality and Regulatory (GxP) standards, including audit trails, traceability and validation expectations. Embedding, influence and adoption Embed with engineering teams for periods of time to understand how they work, introduce the tools and practices, and coach them through the change. Work across organizational boundaries (engineering groups, product teams, central services, Security, Quality, Regulatory and partners) to build alignment and get buy-in for process change. Define and share best practices, reference architectures, patterns and playbooks (e.g. task specification, agent guardrails, review of agent-generated changes and testing expectations) that help teams make development more autonomous. Build communities of practice, run demos and workshops, and present clearly to engineers and to senior executives. Proving and reporting productivity gains Establish baselines before adoption, then measure and show the productivity gains from autonomous development. Report the evidence to senior leaders and use it to guide where to invest next. Gather evidence across four areas, for example: Delivery flow: cycle time, lead time, pull request throughput, and DORA metrics (deployment frequency, change failure rate and time to recover). Example: cycle time from work item to merged pull request before and after agent adoption. Quality and rework: defect escape rate, test coverage, code review turnaround and rework rate. Example: the share of agent-generated pull requests merged without major rework. Developer experience and adoption: developer surveys, active usage by team, and the share

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