About TrueFoundry: TrueFoundry is an enterprise-grade AI/ML platform that accelerates the development, deployment, and scaling of GenAI and ML applications with security, cost efficiency, and cross-cloud flexibility. Trusted by companies like NVIDIA, CVS, Merck, Synopsys, and many more, we're helping enterprises unlock the full value of AI faster.

We're now scaling our Enterprise Outcomes motion a strategic arm focused on delivering domain-specific solutions that drive business transformation and shape our product roadmap.

Role summary You'll design and own core components that enable enterprise customers to run production agentic AI safely and efficiently on TrueFoundry. This includes building robust orchestration for multi-step agents (graph/stateful workflows), model/routing logic, observability and policy enforcement (cost, data residency, rate limiting), and integrating upstream tooling like LangGraph, LangChain, vector stores, and specialized LLM runtimes.

What you'll do

• Architect and implement scalable agent orchestration patterns (graph-based executors, state management, multi-agent coordination) for production workloads.
• Own critical integrations: model adapters, LLM gateway hooks, vector DBs, tools & external APIs, and the platform's LLMops flows.
• Build and improve tracing, benchmarking and observability for LLMs and agents, token/cost accounting, latency p95, throughput, and correctness checks.
• Drive design for safety/guardrails: moderation hooks, human-in-the-loop checkpoints, replayable audit trails and policy enforcement.
• Mentor junior engineers, run design reviews, and improve engineering practices (testing, CI/CD, chaos testing for agents).
• Work directly with strategic customers to prototype complex agentic solutions and translate them into product features.

Must-have

• 4–10 years of software engineering with substantial experience building distributed systems, infra, or ML platforms.
• Deep practical experience integrating and deploying LLMs in production (RAG, retrieval, embeddings pipelines).
• Hands-on experience with agent orchestration frameworks (LangGraph / LangChain or custom agent runtimes) and stateful workflow design.
• Proven track record building observability, cost controls, and policy enforcement for production services.

Preferred / differentiators

• Experience building or contributing to open-source LLM orchestration tools (LangGraph, LangChain, or similar).
• Familiarity with enterprise constraints: on-prem/cloud hybrid deployments, data residency, compliance requirements.
• Background in security, privacy, or model governance for LLMs.
• Demonstrated leadership in cross-functional projects and direct customer engagement.

Qualifications & signals we like

• Open-source contributions, architecture blogs, or public talks on agentic LLMs or LLMops.
• Examples of productizing research or shipping complex infra features.