Analyst, AI ML and Cloud Security

Description

The role sits in the AI, ML and Cloud Security function at the company, a global biopharma business. The function is moving security services across the Chief Security Office from rule-based to AI-driven operation using a multi-agent platform.

The team builds the agent layer, LLM orchestration, the AI and ML platform and the data fabric pipeline, which the company's own security team designs, builds and tests before a managed service vendor runs it. It also sets AI security standards that apply to every AI initiative in the company, advises on cloud security across Azure and GCP, and acts as the link between business units, R&D and the technical AI function. There are seventeen roles across the UK, India and the US.

This is an entry role into AI platform engineering for someone with a scripting and automation background who wants to build depth in AI infrastructure. You will support senior engineers rather than lead workstreams. Much of the work is documentation and test evidence that must be accurate, since the vendor will operate the data fabric from it without anyone from the team present. Curiosity and precision count for more than prior AI experience.

Responsibilities

• Set up and maintain AI platform environments for development and test.
• Record data fabric build outputs and keep the build registry up to date.
• Write CI/CD pipeline documentation detailed enough for the vendor to run the fabric after handover.
• Write automation scripts that monitor pipeline health and flag failures before they hit delivery.
• Write and run test cases for platform and data fabric builds.
• Keep build records current so the platform stays traceable and auditable.
• Work from the architecture specifications provided, and report gaps instead of filling them informally.
• Support senior engineers on the platform and data fabric workstreams.
• Report defects and blockers early, with enough detail to act on.
• Build your knowledge of AI platform engineering and data pipeline delivery.

Requirements

• Experience in AI, platform or software engineering, plus a relevant technical degree, with a focus on AI engineering across the AI platform and data fabric workstreams.
• Experience configuring AI platform environments and supporting LLM, agent and RAG pipeline builds, including environment setup, embedding and vector index jobs, and evaluation runs.
• Experience writing and running test cases for AI components, including prompt-injection, output-validation and regression tests for non-deterministic behaviour.
• Python automation for AI pipeline health covering data quality, ingestion failures, model and API latency, token usage and cost, with automated alerting.
• Experience documenting data fabric builds and CI/CD pipelines to a standard a vendor can take over, keeping build records, working to architecture specifications, and reporting gaps rather than filling them informally.
• Nice to have: experience debugging a failing vector index rebuild and explaining the fix.
• Nice to have: Azure or GCP fundamentals certification such as AZ-900, AI-900 or an equivalent.
• Nice to have: working knowledge of prompt-injection test patterns from the OWASP Top 10 for LLM Applications, enough to extend an existing test suite.
• Nice to have: Terraform and Helm for environment provisioning, with enough Git discipline to work in a shared repository.
• Nice to have: exposure to MLOps tooling such as MLflow, model registries or CI/CD for model deployment.

Conditions

The role is based in the United Kingdom on a hybrid model, with 2 to 3 days a week onsite at the central London headquarters.

The company offers an agile working culture for all roles. If flexibility matters to you, talk to the hiring team about what is possible.

The closing date is 6 November 2026.

The company welcomes applications regardless of background and gives equal consideration to all candidates.

Agencies must have written authorisation from the company before referring candidates. Referrals without it will not be paid.

Requests for interview adjustments go to the company's site. Other enquiries should be checked against the recruitment FAQ guide.