Agentic systems
Design and ship tool-using agents, multi-agent workflows, MCP integrations, memory, evaluation, observability, and recoverable fallbacks.
I join product and engineering teams as a hands-on Contract Lead AI Engineer — turning agentic and LLM use cases into secure, observable systems that run in production.
The useful middle between a strategy consultancy and a narrow implementation resource: senior enough to shape the system, hands-on enough to build it.
Design and ship tool-using agents, multi-agent workflows, MCP integrations, memory, evaluation, observability, and recoverable fallbacks.
Connect LLM applications to real data, APIs, controls, and business workflows — with security, audit, and human review designed in.
Improve the full engineering loop with Claude Code and coding agents: context, planning, implementation, review, testing, and deployment.
Delivered Copilot Studio assistants, voice agents, and AI-driven contact-centre workflows in a regulated environment, including GDPR, redaction, and audit requirements.
Integrated AI translation workflows into an established language-services platform and led delivery across cross-region engineering teams.
Delivered a CI/CD data pipeline using Snowflake, Jenkins, and Airflow to support marketing analytics at enterprise scale.
Map the workflow, data, users, controls, and success measure. Cut the programme to one vertical slice that can prove value.
Build through the real stack — model, tools, API, data, evaluation, observability, review gate, and deployment path.
Use evidence from real runs to improve quality, latency, cost, and control. Leave the team with code, tests, decisions, and a runbook.
Send the outcome, current stack, team shape, location, duration, and IR35 status. I reply within one working day.