Senior, Platform Engineer

About the Role:

GSK's Enterprise AI team builds and deploys AI solutions at scale. Behind every AI product shipped is a shared platform: the Accelerator platform estate, GitHub Copilot, Claude Code/Cowork, Databricks AI Assistant, and Google Cloud data and AI services (BigQuery, Vertex AI, and GKE or Cloud Run). The Senior Platform Engineer (G6) runs this estate, applying Site Reliability Engineering principles, managing strategic vendor relationships, and building the automation and infrastructure-as-code foundations that make the delivery roles faster and safer.

This senior people-manager role leads the platform engineering team and sets its technical direction. Most platform work is AI-augmented: AI coding assistants handle the bulk of routine engineering, freeing the manager and team to focus on architecture, Google Cloud platform strategy, vendor strategy, and reliability. The role's direct customers are the Enterprise AI delivery engineers and the broader population of engineers and data scientists across GSK who build on the platform.

Why This Role Exists:

Platform availability and developer experience determine how fast Enterprise AI can build and deliver AI solutions. Platform outages or poor developer experience compound across every delivery team in the portfolio. The Senior Platform Engineer (G6) is accountable for making sure this does not happen, for building the platform leverage that multiplies the productivity of every engineer, and for leading and growing the team that operates the platform.

• Runs the platform estate that the delivery roles depend on, with SRE discipline, Google Cloud and vendor management depth, and AI-native engineering practice.
• Builds platform leverage through automation, reusable components, and self-service capability that reduce repeated engineering work across the portfolio.
• Leads and grows the platform team by hiring, coaching, and developing engineers toward greater platform ownership, and by raising the team's capability over time.

Key Responsibilities:

Platform Operations & SRE:

• Run the Google Cloud and SaaS platforms that Enterprise AI uses to build AI solutions, including BigQuery, Vertex AI, GKE or Cloud Run, and the Accelerator platform. Ensure availability, security, and cost efficiency.
• Own Google Cloud platform architecture: design and operate BigQuery and Vertex AI environments, including partitioning, reservations, and cost controls, plus Pub/Sub and Dataflow ingestion and Cloud Composer orchestration.
• Apply SRE principles: service-level objectives, observability (Cloud Monitoring, OpenTelemetry), capacity planning, and vendor incident escalation across the platform estate.

AI-Augmented Engineering:

• Use AI coding assistants as the default mode of work; build automations that handle routine platform tasks at scale, using Claude Code, Codex, GitHub Copilot, and Databricks AI Assistant.
• Manage the platform estate through Infrastructure-as-Code (Terraform, GitOps, and CI/CD) so configurations are versioned, auditable, and reproducible across Google Cloud and other platforms.

Developer Experience & Security:

• Treat platform users as customers by delivering self-service capabilities, clear documentation, and fast onboarding for FDEs, FSEs, and data scientists.
• Embed security, data privacy, and AI governance controls so AI solutions inherit compliance rather than building it from scratch. Set the Google Cloud security baseline: IAM and least-privilege access, Cloud KMS (CMEK), VPC Service Controls, Secret Manager, and Organization Policies.

Vendor Management & Standards:

• Act as the technical counterpart to strategic SaaS and cloud vendors (Microsoft, Anthropic, Databricks, and Google) on roadmap, capacity, integration, and incident escalation. Lead the Google Cloud relationship for data and AI services.
• Continuously evaluate emerging AI tooling: pilot what looks promising through fast sandbox proofs of concept, and make adoption or deferral decisions.
• Build and maintain reusable platform components so delivery roles do not reinvent the wheel across engagements.

Team Leadership & Reporting:

• Lead and develop the platform engineering team by hiring, setting objectives, running 1:1s and performance reviews, and developing junior engineers toward greater platform ownership.
• Track and report AI tooling adoption against the organisation's adoption goals for Enterprise AI leadership, and manage platform budget and cloud cost (FinOps) across Google Cloud and SaaS spend.
• Own team delivery and prioritisation: set the team's roadmap and prioritise platform work against delivery-team needs, balancing reliability, security, and developer experience.

Required Experience:

• Overall 15years of professional experience.
• At least 8 years of experience running SaaS or cloud platforms in a production setting.
• Strong proficiency in Python and SQL.
• Hands-on experience with Infrastructure-as-Code (Terraform), GitOps, and modern CI/CD.
• Hands-on production experience with Google Cloud data and AI services (BigQuery, Vertex AI, GKE or Cloud Run, Pub/Sub, and IAM).
• Daily use of AI coding agents (Claude Code, Codex, GitHub Copilot, Databricks AI Assistant) as a primary development mode.
• Experience operating production services with SLOs and on-call practices.
• Experience managing or leading engineers, including hiring, performance management, and development.

Preferred Experience:

• Experience running AI or ML platforms specifically: SaaS Accelerators, AI coding tool estates, integration platforms, or analytics-AI tools.
• Experience as the technical counterpart to strategic SaaS and cloud vendors, managing roadmap, capacity, and incident escalation.
• Experience building agents and automation against operations workflows.
• Familiarity with regulated industry constraints (GxP, including 21 CFR Part 11, GDPR, and SOX) and how to embed them into platform controls.
• Google Cloud Professional certification (Professional Cloud Architect, Professional Data Engineer, or Professional Machine Learning Engineer). Azure or other cloud certifications are also valued.

Education

• Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience. Master’s degree in Computer Science, Distributed Systems, or SRE is advantageous.

Skills

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

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.

Inclusion at GSK:

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 [email protected] to discuss your needs.

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