GenAI Solution Architect

• 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

• 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.

• 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.

• 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.

Originally posted on Himalayas