AI Solution Architect

Role overview

Experience and technical requirements

• 10+ years in technical roles such as Solution Architect, Technical Lead, or Senior Engineer, including recent hands-on production LLM and agentic AI delivery.
• Enterprise architecture and integration experience with APIs, event-driven architectures, data pipelines, distributed systems, and cloud platforms.
• Hands-on experience with LLMs, agent orchestration, RAG, vector databases, and MCP integrations.
• Experience designing secure, scalable, reliable AI solutions aligned with enterprise architecture, infrastructure, and data governance.
• Experience in security-conscious or regulated environments and collaboration with IT Security, Data Governance, Legal, and Enterprise Architecture.
• Proficiency in Python; TypeScript, C#, or Java experience is preferred.
• Understanding of AI evaluation, prompt engineering, model assessment, observability, versioning, and production deployment.
• Strong stakeholder management and communication, including translating business needs and explaining architecture to executives.
• Machine learning or data science background is advantageous but not mandatory.

Key responsibilities

• Discover AI use cases, assess feasibility, qualify solutions, evaluate technology, and plan implementation with business stakeholders.
• Own end-to-end LLM and agentic AI architecture, including orchestration, RAG, MCP, tool calling, and enterprise integrations.
• Design secure, scalable, reliable, maintainable architectures aligned with enterprise standards.
• Prototype solutions, validate architecture, evaluate feasibility, and mitigate delivery risks.
• Benchmark models, platforms, frameworks, and tools against real business scenarios; advise on adoption and build-versus-buy choices.
• Define reusable AI architecture standards and patterns, and establish practices for evaluation, observability, versioning, release, and monitoring.
• Lead and mentor engineers, review designs and code, and resolve technical challenges.
• Work with security, governance, legal, and architecture teams on privacy, compliance, auditability, and responsible AI.
• Track AI initiatives, priorities, roadmaps, reusable components, and measurable business value; communicate trade-offs, risks, and benefits.

Expected first-year outcomes