Solution Architect, Forward Deployed AI

Responsibilities: Partner with client business, product, engineering, security, and operations to clarify requirements and record architecture decisions Design end-to-end AI solutions covering RAG, agentic workflows, MCP tool integration, IAM, enterprise integration, LLM security, observability, and evaluation Develop Cloud FinOps cost models linking feature cost, usage, quality, and business value Document AI governance evidence for each agent, including autonomy limits, tool permissions, human checkpoints, results, and rollback controls Model domain knowledge graphs covering entities, relationships, provenance, owners, applying GraphRAG for better retrieval Create Python reference implementations and proofs with Forward Deployed Engineers and oversee pilot-to-production transition under Architecture Governance Publish reusable patterns for scalable AI deployments

Key requirements: Client-facing solution architecture experience with production AI or business software Strong fundamentals in cloud computing, microservices, APIs, data flows, IAM, and enterprise integration patterns Practical knowledge of LLM apps, Prompt Engineering, RAG, AI agent frameworks, MCP tool calling, and agent orchestration Recent hands-on Python coding to prototype and validate AI implementations Experience defining LLM evaluation, AI observability, LLM security controls, and governance concepts including autonomy, oversight, auditability, and risk Clear architecture documentation and strong communication with stakeholders; comfortable with ambiguity in regulated environments client-facing communication collaboration leadership Cloud computing architecture microservice architecture APIs and data flows