Senior/Staff Applied GenAI Researcher – Enterprise Outcome Team

We're TrueFoundry, and we're building it. We're looking for a Senior/Staff Applied GenAI Researcher – Enterprise Outcome Team to join the team.

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.

• →Build and productionize LLM-based and ML-based solutions, utilizing both open-source and proprietary models
• →Integrate TrueFoundry’s platform seamlessly into customer environments and leverage it to expedite the time to value of developing these applications 
• →Build agents, write prompts, eval sets, optimize inference time and response quality for applications  
• →Write maintainable production-quality high-performance code frequently in Python
• →Build and optimize REST APIs, gRPC services, and data pipelines
  
• →Drive rapid feedback loops from customer deployments into continuous improvements for product and platform
• →Participate in solution architecture design, code reviews, and engineering best practices adoption

• 4+ years experience building and deploying ML applications in production. 
• 4+ years experience writing production code in python 
• 2+ years working in deep learning and Natural language processing
• 1+ year experience building Agentic applications and GenAI Apps
• Experience building REST APIs, working with Docker, and setting up CI/CD pipelines
  

• Working knowledge of model servers like vLLM, Triton, TensorRT is preferred
• Understanding of Kubernetes, distributed systems architecture, and cloud-native technologies is preferred 
• Strong system design abilities, with a focus on modular, reliable, and scalable architecture
• Passionate about applying AI to solve real-world, cross-industry problems
• Familiarity with LLM fine-tuning, RAG (Retrieval-Augmented Generation), prompt engineering, or evaluation frameworks