AI Architect

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it's medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.

Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity.

As a Principal AI Architect, you will own the architecture underneath every FourKites AI agent: the harness they run in, the context they see, the models they call, and the evals that tell us whether they work. Our digital workers (Tracy for track and trace, Sam for supplier operations, Alan for appointment management, Cassie for customer service, Polly for document management, and more on the way) do real supply chain work on a shared platform, and you will set its direction. This is a hands-on individual contributor role based in Chennai (hybrid). You'll write code most weeks, lead design reviews across agent teams, and make the calls on model choice, context strategy and evaluation that every team builds on.

What you’ll be doing:

• Define the reference architecture for FourKites agents: the agent loop, tool execution, state and checkpointing, retries and recovery, guardrails, permissions, and human-in-the-loop handoffs
• Design for long-running, multi-step work that spans many context windows and recovers cleanly when a tool, carrier API or model call fails
• Decide where each problem is best solved (frontier LLM, open-weight model, fine-tuned encoder, or plain code) and own model routing, cost-per-task and latency budgets across the fleet
• Integrate agents with FourKites' event-driven platform (Kafka, microservices, Postgres) and with customer systems through APIs and MCP servers
• Set the standard for what goes into an agent's context window (system prompts, tool definitions, retrieved documents, memory, prior turns) within an explicit token budget
• Build shared components for retrieval, compaction and summarization, sub-agent isolation, prompt caching and persistent memory so teams stop reinventing them
• Diagnose context failures (lost instructions, tool confusion, quality degradation as the window fills) and turn fixes into reusable harness patterns
• Build the offline eval framework: golden datasets from production traces, code-based checks, LLM-as-judge graders calibrated against human labels, and CI regression suites that gate every prompt, model or tool change
• Build online evaluation: scoring sampled production traffic, trace- and task-level quality metrics, drift detection, shadow and A/B rollouts, user feedback signals, and quality alerts