Technical Account Manager (Strategic Accounts)

About the role

You will own a portfolio of enterprise accounts end-to-end - from driving adoption with platform engineers to expanding revenue with executive sponsors. You will be the primary point of contact for some of TrueFoundry's most strategic customers, responsible for retention, growth, and making sure they get maximum value from the platform.

Responsibilities

• Own account health, renewal, and expansion across a portfolio of enterprise customers
• Identify and execute upsell and cross-sell opportunities within existing accounts
• Drive product adoption by working closely with customer engineering and data science teams
• Manage multi-threaded stakeholder relationships from practitioners to C-suite
• Partner with Product and Platform Engineering to close the feedback loop on customer needs
• Prepare account reviews, success plans, and executive-level communications

Requirements

• 5+ years in technical account management, customer success, or sales at a B2B SaaS or infrastructure company
• Technically fluent - comfortable discussing ML infrastructure, cloud, and developer tooling with engineering teams
• Proven track record of expanding revenue within accounts, with specific deals you can speak to
• Strong communicator, high ownership, comfortable navigating ambiguity in a fast-moving startup
• Experience managing enterprise accounts with multiple stakeholders across technical and business functions

Benefits

Additional Information

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. 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 - a 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.

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Company Intel

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