Technical Architect

Role Summary We're looking for a Technical Architect who combines strong product engineering fundamentals with deep, hands-on expertise in agentic AI systems. This is not a research role — it's an architecture and delivery role for someone who has built and shipped real products, and who now wants to architect multi-agent, LLM-powered systems that go into production for enterprise clients. You'll own technical design end-to-end: from client conversations through architecture decisions to code reviews and production readiness. Key Responsibilities Architect and oversee delivery of product-grade software systems, with agentic AI (multi-agent orchestration, tool-use, RAG, memory systems) as a core capability area Design and review system architecture for scalability, reliability, security, and cost — balancing agentic AI capability with production engineering discipline (observability, testing, CI/CD, versioning) Lead technical design for client engagements: translate ambiguous business problems into agent workflows, orchestration graphs, and system architectures Evaluate and select frameworks (LangGraph, LangChain, AutoGen, CrewAI, or custom orchestration) based on the problem, not fashion Set up evaluation, guardrails, and observability for LLM/agentic systems (e.g., Langfuse or equivalent) to ensure production reliability Mentor engineering teams on both classical product engineering practices and emerging agentic AI patterns Partner with presales/architecture leads on solutioning, estimation, and proof-of-concept builds for prospective clients Stay current on the LLM/agentic ecosystem (Claude, GPT, open models, MCP, agent protocols) and bring pragmatic recommendations — not hype — into client and internal conversations Own technical quality bar: code reviews, architecture reviews, and production readiness checks across projects Required Skills & Experience Strong product engineering background: has designed, built, and shipped full-stack or backend-heavy products at scale (not just prototypes) Hands-on experience building agentic AI systems in production — multi-agent orchestration, tool calling, RAG pipelines, memory/state management Proficiency with at least one agent orchestration framework (LangGraph strongly preferred; LangChain, AutoGen, CrewAI acceptable) Solid grounding in software architecture fundamentals: distributed systems, API design, cloud-native deployment (AWS/Azure/GCP), containers (Docker/Kubernetes) Working knowledge of LLM APIs (Anthropic Claude, OpenAI, or equivalent) and prompt/context engineering at a systems level Experience with LLM observability/evaluation tooling (Langfuse, LangSmith, or similar) Strong programming skills in Python and at least one other language (TypeScript/Java/Go) Comfortable operating in a client-facing services environment: can explain architecture trade-offs to both engineers and business stakeholders Preferred / Nice-to-Have Prior experience with AWS Bedrock or similar managed LLM infrastructure Exposure to MCP (Model Context Protocol) or emerging agent interoperability standards Experience in a technology services/consulting environment with multiple concurrent client engagements Anthropic or OpenAI certifications/partner-track credentials Prior startup or 0-to-1 product-building experience Has architected and shipped at least one agentic AI system into production for a client Has established (or improved) engineering practices around agent evaluation, observability, and reliability Is a trusted technical voice in client presales conversations has context menu