Agentic AI Engineer (AWS)

Agent Platform Engineering Design, build and maintain production-grade AI agents, orchestration workflows and integrations that automate end-to-end security alert triage and investigation.

LLM, Agent & Security Architecture Make sound design decisions across models, agents, workflows and security controls, ensuring systems are reliable, secure and resilient to adversarial input.

Evaluation & Observability Develop evaluation frameworks, quality gates, tracing and monitoring capabilities that ensure agent behaviour is measurable, explainable and continuously improving.

Platform Engineering & Operations Own the AWS infrastructure underpinning the platform, delivering and operating it through infrastructure as code, CI/CD and production monitoring.

Technical Leadership Mentor engineers, review code, establish engineering standards and help grow agent engineering capability across the team.

Stakeholder Collaboration Work closely with SOC analysts, Cyber AI teams and platform engineers to deliver solutions that meet operational and business needs.

AI Enablement Support broader AI initiatives and contribute to the adoption of AI engineering best practices across the organisation.

Documentation, Risk & Compliance Maintain clear documentation and ensure security, privacy and compliance requirements are embedded throughout the platform lifecycle.

Enough about us, let's talk about you

You are an engineer first. You write real, tested, typed Python and you are at home in a modern toolchain. You have moved beyond wiring agents together and can reason about the trade-offs: when to use an agent, how to make it reliable, how to prove it works, and how to run it safely in production. You can take a body of work from idea to deployed reality on your own, and you raise the people around you as you go. Required skills

5+ years of relevant engineering experience, with demonstrable experience building and shipping production software, and hands-on experience building AI agents or LLM-powered applications.

Strong Python engineering practices, with type hints, sensible use of classes and dataclasses, decorators and context managers, and clean project structure. Comfortable with the modern toolchain (uv, ruff, mypy, pytest, pre-commit).

Experience writing tests, including mocking external services, and you build and maintain CI pipelines (Azure DevOps).

Practical understanding of how LLMs and agents work (tokens, context, prompting, structured output, tool use, RAG and multi-agent patterns) and hands-on experience building agent runtimes, orchestration frameworks and production AI agents, ideally with Strands or a comparable framework.

Solid experience with AWS, including Bedrock. Experience with AgentCore, or the ability to get there quickly, is a strong plus.

Proven ability to define and ship infrastructure as code, ideally with AWS CDK in Python and Terraform.

Awareness of prompt injection and the risks of untrusted input, and practical experience defending against them with guardrails, input validation, and least privilege design.

Experience evaluating non-deterministic systems (eval sets, LLM as judge) and instrumenting them so their behaviour can be traced and understood.

A track record of owning and delivering complex technical work end to end, with sound judgement on design trade-offs.

Ability to mentor engineers, review code constructively, and explain technical decisions clearly to both technical and non-technical audiences.