Responsibilities
- Design and implement scalable, reliable AI agents and copilots using modern LLM frameworks. Define patterns for orchestration, tool usage, memory, and human-in-the-loop workflows. Provide technical governance and best practices for agent-based systems.
- Strong experience working with large language models (e.g. OpenAI, Azure OpenAI), prompt engineering, embeddings, vector databases, and retrieval-augmented generation (RAG). Ability to evaluate and optimise model performance for real-world use cases.
- Build agents that integrate with internal systems (APIs, databases, SaaS tools such as Microsoft 365, Notion, etc.) to automate workflows and enhance productivity.
- Design intuitive copilot experiences that augment users rather than replace them, with a focus on usability, trust, and explainability.
- As a senior member of a growing team, support other engineers through mentoring, documentation, and establishing best practices in AI engineering.
- Define metrics and evaluation frameworks for agent performance, reliability, and safety. Continuously iterate based on feedback and usage data.
- Experience deploying AI systems into production environments, including monitoring, observability, cost control, and lifecycle management of models and agents.
- Experience of working within a project team in an Agile manner, collaborating closely with stakeholders to iteratively deliver value.
- Ensure AI solutions adhere to security, privacy, and data governance standards. Awareness of risks such as hallucination, data leakage, and misuse, with mitigation strategies in place.
- Liaise with the relevant Business Analyst, Quality Assurance Team, Security Team, Infrastructure Team and stakeholders on matters as required.
Requirements
- Experience building AI agents, copilots, or automation workflows using LLMs in a production or enterprise environment.
- Strong understanding of prompt engineering, RAG architectures, and tool-augmented agents.
- Experience integrating AI solutions with APIs, databases, and enterprise systems.
- Familiarity with TypeScript.
- Education in an appropriate field of study or equivalent work experience.
- Experience of leading projects or owning AI-driven initiatives end-to-end.
- Experience of managing matters autonomously. Strong technical awareness and the ability to think strategically about AI adoption within an organisation.
- Outstanding attention to detail and strong analytical and research skills.
- Excellent written and verbal communication skills, with the ability to explain AI concepts to non-technical stakeholders.
- Excellent work ethic and ability to cope with pressurised situations and tight timeframes whilst maintaining a professional approach.
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