Artificial Intelligence Architect

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 youll 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

Connect eval metrics to the business outcomes customers pay for, such as tracking accuracy, exceptions resolved without a human, and appointment fill rate

Review designs and PRs for agent teams, write the architecture docs and playbooks they follow, and mentor staff and senior engineers

Track the model and tooling landscape, run the experiments that matter, and give leadership clear build, buy or wait recommendations

Partner with Product, Solutions and Customer Success to turn field failures into platform fixes

About the team:

Our product and engineering teams are dedicated to providing the industry's best-in-class end-to-end supply chain visibility platform. The agent platform sits underneath every FourKites digital worker, and the teams building on it ship agents that track freight, chase suppliers, book dock appointments, answer customer questions and read shipping documents in production. You'll be the senior technical authority for how FourKites builds, ships and measures agents, working closely with agent teams in Chennai and with colleagues in the US and Europe.

Who you are:

Agents in production : you've architected and operated at least one LLM agent system with real users and real traffic (tool calling, multi-step workflows, failure handling, human escalation) and owned it after launch through incidents, cost and latency. Framework demos and POCs alone won't be a fit

Deep ML knowledge of encoders and transformers : attention, positional encodings, encoder-only vs decoder-only vs encoder-decoder, tokenization and the KV cache. You've trained or fine-tuned encoder models (BERT-family, sentence embeddings, bi-encoders, cross-encoder re-rankers) for classification, extraction or retrieval, and understand how contrastive training, distillation and quantization change cost and quality

Context engineering :you've managed token budgets, built retrieval and memory for agents, used compaction and sub-agents to keep long tasks coherent, and measured how quality changes as the window fills

Harness engineering: you've built the execution layer around a model: agent loop, tool dispatch, state and checkpoints, interruption and recovery, sandboxing and permissions, guardrails and model routing. Fluent with MCP, structured outputs and streaming

Hands-on online and offline evals : you've built eval suites yourself: datasets, graders, LLM-as-judge calibration and CI regression gates offline; production scoring, trace sampling, drift monitoring, shadow deployments and feedback loops online. You've used evals to block a release or justify a model switch

10+ years building software or ML systems, with 3+ years building on LLMs

Python: strong, hands-on. Comfortable reading and reviewing Java or TypeScript services

Production experience on AWS or GCP, containers and Kubernetes, and event-driven systems

Excellent oral and written communication skills: you can explain a trade-off to an engineer and its business impact to an executive

Nice to have:

Experience in supply chain, logistics or transportation

Document AI: OCR, layout models, and extraction from bills of lading, invoices and PODs

Voice or email agents that act on behalf of a business

Agent security: prompt injection defence, tool permissioning, and tenant data isolation

Hands-on experience with several model families (Claude, GPT, Gemini, open-weight models) and serving stacks

Published work, talks or open-source contributions on agents, retrieval or evaluation