Lead AI Solution Architect

Role overview Lead the architecture and delivery of complex AI systems that connect to established enterprise platforms and operate reliably at production scale. You will design the overall solutions, build critical components, guide engineering teams, and help clients introduce AI into software delivery with appropriate human oversight.

Responsibilities - Design AI platforms that combine agents, retrieval from enterprise data, and integrations with business systems, data platforms, event streams, and legacy APIs. - Plan for performance, cost, tenancy, data location, and failures across models, tools, and upstream services. - Define secure ways for agents to access tools and communicate, including identity, authorization, and audit controls. - Develop AI-supported delivery workflows in which agents help produce plans, code, tests, documentation, and reviews, with approval gates and traceable quality measures. - Work with engineering, security, and architecture teams to identify useful AI applications and explain where they are unlikely to help. - Design for regulated settings, measure outcomes such as delivery time and defect rates, and communicate trade-offs to technical and executive audiences. - Set technical direction through architecture reviews, reference designs, mentoring, and decision records.

Requirements - At least 10 years in software engineering and architecture, including designing and operating enterprise systems in production. - Strong integration and systems design experience, including APIs, event-driven systems, messaging, OAuth 2.0 or OIDC, and legacy environments. - Practical experience building production LLM systems, such as retrieval-augmented generation, tool calling, agent orchestration, evaluation, and observability. - Regular use of AI-assisted engineering tools, with judgment about their benefits and risks. - Working knowledge of ML concepts such as embeddings, model selection, evaluation, and drift. - Experience with security reviews, audit trails, and change controls in regulated environments; clear spoken and written English.

Nice to have - Customer-facing technical leadership; production experience with agent frameworks, MCP or A2A, and vector or graph stores. - Practical AI governance, model risk, red-teaming, or guardrail experience. - Deep experience with a major cloud platform, DevSecOps, CI/CD, infrastructure as code, or policy as code.