Artificial Intelligence Engineer

Responsibilities

• Design, build and operate multi-step agentic pipelines that create virtual delivery capacity, surfacing work for engineers to review, accept, or reject.
• Build and maintain the agent types that make up Company's virtual delivery team, including but not limited to: developer agent, security review agent, regression test-plan agent, grooming agent, and criteria cross-check agent, and expand the catalogue as new automation opportunities are identified.
• Develop AI-powered product features and tooling in Python, contributing directly to the build alongside squads rather than purely in an advisory or enablement capacity.
• Evaluate every workflow step and make an explicit decision about whether it calls for deterministic logic (rules, conditions, structured code) or a non-deterministic AI model, documenting the rationale so the team can audit and improve it over time.
• Integrate AI-first automation into the full SDLC: code generation and review, test-case creation, PR triage, dependency management, release notes, and incident summarisation.
• Act as an enabler across engineering squads, partnering with engineers and squad leads to help each team identify, scope, and own their own agentic workflows with the right guidance and tooling.
• Develop and share evaluation patterns, reference implementations, and best-practice guardrails that individual engineers and squads can adopt and adapt, keeping ownership of agents with the teams that run them.
• Evaluate and onboard orchestration frameworks (such as LangChain, AutoGen, or the Claude Agent SDK) and make principled tool-selection decisions that the broader engineering organisation can build on.
• Own AI cost efficiency across the agentic squad, monitoring token usage, model selection, caching strategies, and prompt optimisation to ensure company gets maximum value from its AI investment without unnecessary spend.
• Coach the engineering team on agentic patterns, prompt engineering, and when not to use AI, building capability and confidence across the organisation as adoption grows.

Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, AI, or a related field.
• 10+ years of software engineering experience, including 2+ years building LLM or AI-powered solutions in production.
• Strong understanding of when to use traditional software engineering versus AI/LLM-based approaches, with the ability to communicate technical decisions clearly.
• Hands-on experience building production-grade AI agents or multi-step AI workflows using LangChain, AutoGen, CrewAI, Claude Agent SDK, or similar frameworks.
• Strong Python development skills.
• Practical experience with prompt engineering, RAG, tool/function calling, structured outputs, vector databases, and embeddings.
• Experience applying AI automation across the SDLC, such as code reviews, test generation, PR triage, and CI/CD.
• Experience evaluating and monitoring AI/agent performance and improving output quality.
• Cloud experience, preferably Azure, with an understanding of AI inference cost, performance, reliability, and scalability.