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We are looking for an Architect / Principal Software Engineer to lead the technical direction of our AI Engineering pods. You will split your time between hands-on engineering (about 30% to 40% prototyping core workflows, building reference implementations, and reviewing code) and higher-level system architecture, technical standards, and mentorship across teams.
You will work closely with US-based Product, Security, Data, and Infrastructure teams to make sure our agentic systems are reliable, fast, and secure. Your main focus will be designing predictable multi-agent workflows, low-latency LLM orchestration services, and retrieval pipelines that run cleanly in production.
Req.#1098579355
- Set architectural direction across multiple AI pods, making clear trade-offs between model capabilities, cost, latency, and operational complexity
- Spend roughly a third of your time in code: writing backend AI services, building orchestration patterns, and defining integration contracts with frontend teams
- Design agent workflows with streaming responses, predictable error handling, and low latency for chat and automated tasks
- Establish shared standards for Model Context Protocol (MCP) servers, tool schemas, and context management
- Work with Okta Security and Infrastructure to enforce least-privilege tool execution, tenant isolation, and secret handling within agent loops
- Define observability requirements and service-level objectives, including tracing with tools like LangSmith or Arize Phoenix
- Mentor Staff and Senior engineers through direct design reviews and technical guidance
- 10+ years of software engineering experience, with a background in designing, scaling, and operating distributed backend systems
- 6+ years of Python experience, especially with FastAPI, AsyncIO, and modern typing practices. Python is our primary language for backend and AI services
- 3+ years building production systems with LLMs, including LangChain, LangGraph, AWS Bedrock, Anthropic Claude, or OpenAI APIs. Candidates with classical ML backgrounds (PyTorch, scikit-learn) who moved into generative AI architectures are welcome
- Hands-on experience with LangGraph or similar stateful agent frameworks, particularly managing state graphs, loops, checkpointing, and human-in-the-loop steps
- 3 to 4 years of React and TypeScript experience, enough to review UI code, understand frontend state, and ensure streaming APIs work cleanly with the web client
- Production AWS experience, including EKS or ECS, Lambda, Bedrock, S3, Docker, and Terraform or OpenTofu
- Authentication and security fundamentals, specifically OAuth 2.0, OIDC, and secure credential handling
- Practical experience building custom MCP servers and defining tool discovery patterns
- Experience migrating from older retrieval systems (such as Amazon Kendra) to vector platforms like OpenSearch, Pinecone, or Qdrant
- Experience handling multimodal data (images, documents) within LangGraph state
- Background in Identity and Access Management (IAM) or Customer Identity (CIAM)