GenAI Solution Architect

Job Description This is a remote position. We are seeking a very hands-on GenAI Architect to take sole architectural ownership of a live, commercially deployed agentic product. A global consumer brand has already launched a GenAI-powered front-end agent in the UK, enabling customers to discover and explore products through natural language conversation. The platform is now entering a rapid multi-market roll-out: Germany first, then the Czech Republic, then Romania, with Poland and other markets running in parallel.This is a backfill in a formed, functioning squad - the product is in production and the delivery rhythm is established. You are joining to strengthen the team, not to rebuild it. About the role: You will be embedded in the development team, and your job is to stay ahead of it. New feature requirements arrive incomplete: you are the person who discovers the unknowns inside them, identifies where the technology risk actually sits, and resolves both before they turn into development issues mid-sprint. You prove your designs rather than presenting them. Architectural decisions here are validated by delivering a working proof of concept, which the development team then takes forward to full implementation. The measure of success is simple: developers move faster because you were there first. Tech stack: Python, LangChain for LLM orchestration, LangGraph, LangSmith AWS Bedrock for model access (Anthropic among the providers), with Bedrock Knowledge Bases and Bedrock Guardrails AWS: ECS, Lambda, S3, API Gateway, DynamoDB, Cognito, EventBridge, WAF Twilio (telephony, in production) MCP and Agent-to-Agent integrations What you'll be doing: Owning the AI architecture underpinning the multi-market roll-out; Discovering the unknowns in new feature requirements — surfacing ambiguity, hidden assumptions, and unstated constraints before implementation starts; Identifying and mitigating technology risks early, well before they surface as development problems; Proving designs by building proofs of concept, and handing them to the development team as the basis for full implementation; Shaping the evaluation and observability architecture for the agentic system — this is a new area of the solution, and you will be expected to bring options and a point of view rather than inherit a design; Designing and iterating on multi-agent architectures, and defining integration standards for MCP servers and agent-to-agent communication; Working with the senior technical lead in Poland and the development team in India, supporting implementation day to day; Collaborating with the data and domain architects on the wider programme to deliver joined-up solution designs; Working closely with the Business Analyst to validate business designs and provide technical feedback; Advising on prompt engineering strategy, tool use design, and agent reliability across multiple languages and markets. Requirements Advanced, production-level experience with LangChain, LangGraph, and LangSmith in Python; Hands-on experience implementing Model Context Protocol (MCP) and Agent-to-Agent (A2A) integrations; Solid experience with AWS, ideally including Bedrock, and comfort designing within a serverless and container-based AWS estate; A genuinely hands-on approach — you build the proof of concept yourself rather than delegating it; A considered view on GenAI evaluation and observability, and the ability to argue for an architectural approach in front of technical stakeholders; Proven ability to work with under-specified requirements: extracting the real need from stakeholders and making defensible decisions under uncertainty; Sound judgement in technology selection, with a track record of de-risking choices early rather than discovering problems in development; Ability to work effectively with a distributed, cross-timezone team and to communicate designs clearly to mid-level and junior engineers; Strong communication skills — able to translate complex AI concepts for technical and non-technical stakeholders; English proficiency at B2 level or above. Nice to have: Experience with telephony and call centre applications, particularly Twilio and voice/AI integration patterns; Experience scaling an AI product across multiple languages or markets; Background in enterprise AI platform architecture.