TrueFoundry
ForwardDeployedEngineer-GTM
Neural analysis suggests this role is
optimal for Senior candidates.
“Forward Deployed Engineer - GTM at TrueFoundry. Skills: AI, ML, DevOps, Cloud. Own technical arc of deal. Conduct discovery and demo”
What You'll Achieve.
Deliver impact; Win POC on technical merit; Turn evaluation into contract
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
3 to 7 years backend software engineering experience, Python, Go, or TypeScript experience, Comfortable with Kubernetes or major cloud
Nice to Have
Familiarity with vLLM, SGLang, LangChain, Triton, model serving
What You'll Do.
Own technical arc of deal
Conduct discovery and demo
Lead architecture discussion
Write integration glue
Write agent workflows
Make architecture calls
Act as technical decision-maker
Bring structured asks to engineering
Bring data-backed asks to product
How You'll Work.
Team & Collaboration
Customer engineering leaders; TrueFoundry engineering; TrueFoundry product
Communication Scope
Technical presentations
Full Job 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're TrueFoundry, and we're building it. We're looking for an Account Executive (Focus on Enterprise Sales) 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 We've built two products to solve this: AI Gateway is the control plane five composable components (Prompts, LLM Gateway, MCP Gateway, Guardrails, Agent Gateway) that handle routing, orchestration, and governance. AI Deploy is the compute layer of Kubernetes-based platform that abstracts ML workloads as standard software primitives, so everything runs on unified infrastructure. 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. What you'll do Own the technical arc of the deal: discovery and the in-depth demo, the architecture discussion with the custome
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