1 month ago
Remote, United Kingdom or Leeds, United KingdomSenior
Responsibilities
- Design and develop AI-enabled microservices, APIs, applications, internal tools, and automation workflows.
- Integrate AI capabilities from providers such as OpenAI and Anthropic into secure, scalable engineering systems.
- Enhance Power Automate, n8n, and Workato workflows with intelligent processing and agentic patterns.
- Implement Model Context Protocol servers for secure AI-to-system connectivity.
- Build AI agents and retrieval-augmented workflows with tools, memory, orchestration, context control, and guardrails.
- Lead AI-based document parsing and intelligent data extraction initiatives.
- Implement AI observability practices to monitor model behavior, detect anomalies, and assess performance and reliability.
- Evaluate when deterministic solutions are safer, simpler, or more cost-effective than AI solutions.
- Advise business stakeholders, delivery teams, engineering, testing, architecture, and citizen developers on effective AI adoption.
- Mentor staff, deliver demonstrations, create reference documentation, and develop AI engineering best practices.
- Contribute to AI governance, ethics, compliance, security, data governance, human-in-the-loop controls, and risk mitigation.
Requirements
- At least 5 years of development experience across APIs, integrations, microservices, or full-stack development.
- Degree in Computer Science, IT, Engineering, or a related discipline, or equivalent practical experience.
- Hands-on software development experience with one or more of .NET/C#, Python, or TypeScript.
- Strong understanding of AI, machine learning, LLMs, prompt engineering, embeddings, retrieval-augmented generation, agentic workflows, and context windows.
- Proven ability to integrate AI into both low-code automation flows and high-code applications, APIs, microservices, distributed systems, and development or testing tools.
- Demonstrated real-world portfolio showing applied AI skills, delivered solutions, and measurable impact, including personal or open-source AI projects where applicable.
- Experience integrating AI into workflows and systems across low-code and high-code platforms.
- Hands-on experience with AI coding assistants such as GitHub Copilot or Claude Code and autonomous software engineering agents.
- Exposure to RAG, vector databases, embeddings, AI retrieval systems, cloud AI services, and AI-agent orchestration.
- Exposure to DevOps practices, CI/CD pipelines, infrastructure-as-code, and Azure or AWS in regulated enterprise environments.
- Extensive experience with source control and version management systems.
- Strong understanding of responsible AI, governance, bias mitigation, compliance, and risk-based decision-making.
- Ability to communicate complex AI concepts to technical and non-technical audiences, mentor developers and testers, and collaborate with stakeholders.
- Professional certifications in AI fluency or specialized AI/ML technologies are advantageous.
Tech Stack
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