Senior AI/ML Engineer — LLM & Agent Stack (Customer Facing)
TrueFoundry
Salary undisclosed
Need a reasonable accommodation to apply or interview? Contact us.
JobMinglr uses automated technology to recommend jobs based on profile information and job preferences. Match Score does not determine eligibility for a position, prevent a user from viewing or applying to a job, or make hiring decisions on behalf of an employer.
Description
About TrueFoundry
Every production AI system, whether it's powering customer support, writing code, analyzing financial data, or diagnosing medical conditions, needs the same foundational infrastructure.A way to route between models. A way to manage tools and integrate them securely. A way to orchestrate agents and enforce governance. A unified compute layer to run it all.
That infrastructure layer is being built right now.
We are looking for a Senior AI/ML Engineer — LLM & Agent Stack (Customer-Facing) to join the team.
The Problem We're Solving
Companies are moving beyond simple chatbots to production agentic systems. These systems route between OpenAI, Anthropic, Google, and self-hosted models. They integrate dozens of tools via protocols like MCP. They orchestrate multi-agent workflows where agents coordinate with other agents.
The infrastructure to support this doesn't exist yet. You can't just duct-tape together a few API calls and call it production-ready.
You need a control plane that handles:
- Intelligent routing with observability, cost policies, and fallback logic
- Centralized tool and MCP server management with security and lifecycle controls
- Agent orchestration with governance and guardrails
- A unified compute layer to run self-hosted models, custom tools, and agents
AI Gateway is the control plane: five composable components (Prompts, LLM Gateway, MCP Gateway, Guardrails, Agent Gateway) that handle routing, orchestration, and governance.
We're Series A, backed by Intel Capital and Sequoia. Companies like CVS, Mastercard, Siemens, Paytm, Synopsys, and Zscaler run production AI workloads on our platform.
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. This is a customer-facing role where you'll work closely with TrueFoundry's enterprise customers. You may also need to write code in their environments.
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
5–10 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.
Strong systems knowledge: Kubernetes, container orchestration, service meshes, and performance tuning.
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.
Traits we are looking for: Ownership, ability to execute, hustle and think out of the box, data-driven decision making, be comfortable with more unknowns than knowns.
Perks of Working at TrueFoundry
- Join a fast-growing Series A, Bay Area-based startup building cutting-edge AI infrastructure.
- Comprehensive health insurance for you and your family, including medical, dental, and vision coverage.
- 401(k) retirement plan.
- Flexible hybrid work, 2 days a week in the office (Tuesday & Wednesday), with flexibility around the schedule.
- Lunch and snacks are on us whenever you come into the office.
- Work from another TrueFoundry office for up to 1 month each year, whether that's our London or India office, if you'd like a change of scenery.
Benefits
- Join a fast-growing Series A, Bay Area-based startup building cutting-edge AI infrastructure.
- Comprehensive health insurance for you and your family, including medical, dental, and vision coverage.
- 401(k) retirement plan.
- Flexible hybrid work, 2 days a week in the office (Tuesday & Wednesday), with flexibility around the schedule.
- Lunch and snacks are on us whenever you come into the office.
- Work from another TrueFoundry office for up to 1 month each year, whether that's our London or India office, if you'd like a change of scenery.
About this role
TrueFoundry is building the control plane for production agentic AI systems—the infrastructure layer that enterprises need to safely orchestrate multi-agent workflows, route between models, manage tools, and enforce governance at scale. As a Senior AI/ML Engineer on the LLM & Agent Stack team, you'd own core components that make this possible: designing agent orchestration patterns, building model routing and policy enforcement, and creating observability for production LLM workloads. This is hands-on work spanning graph-based executors, state management, cost accounting, safety guardrails, and integrations with frameworks like LangGraph and LangChain.
The role sits at the intersection of infrastructure and customer problems. You'll work directly with enterprise customers like CVS, Mastercard, and Siemens to prototype complex agentic solutions and translate them into product features—sometimes writing code in their environments. You'll also mentor junior engineers and drive engineering practices across testing, CI/CD, and chaos testing for agents. The position calls for 5–10 years building distributed systems or ML platforms, deep production LLM experience (RAG, embeddings, retrieval), hands-on work with agent orchestration frameworks, and strong systems knowledge around Kubernetes and performance tuning. Open-source contributions to LLM tools, familiarity with enterprise deployment constraints, or a background in security and model governance would set you apart.
This is a San Francisco in-office role (despite the job posting noting flexible hybrid, the listing specifies in-office). You'd join a Series A backed by Intel Capital and Sequoia, with standard startup benefits including health coverage, 401(k), and the ability to work from other TrueFoundry offices for up to a month per year.
This role's local market on JobMinglr
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Senior AI/ML Engineer — LLM & Agent Stack (Customer Facing) postings in San Francisco, and analyze any offer before you accept it.
Before you apply, worth reading
How JobMinglr reads this job
Every listing here is scored against your profile before you apply: skills overlap, experience level, location and work arrangement, each weighted and explained. You see the score and the reasons, not just a list. How the matching works.