Join Sarvam, a trailblazer in developing India's sovereign AI platform. We are building the foundational AI infrastructure, from research and models to applications, with a mission to make AI truly work for India.

Partnering with leading enterprises and public institutions, including Tata Capital, SBI Life, CRED, IDFC, and LIC, we are backed by top venture capital firms like Lightspeed, Peak XV, and Khosla Ventures. This is an opportunity to be part of a high-talent-density team working on impactful, frontier AI challenges.

We are seeking a Strategic Deployment Engineer to be our on-the-ground technical expert. You will be embedded with clients, taking ownership of the entire AI system deployment lifecycle, even in highly secure and complex environments where standard approaches don't apply. Your role is pivotal in ensuring system functionality, fostering client trust, and building lasting capabilities.

As a Strategic Deployment Engineer, you will:

- Take full ownership of deploying Sarvam's comprehensive AI stack within client environments, including on-premises, air-gapped, and classified infrastructure. - Act as the primary technical point of contact for your assigned accounts, guiding them from initial scoping and Proof of Concepts through to stable, operational phases. - Independently diagnose and resolve complex issues across integration, model performance, and infrastructure without reliance on escalation teams. - Gather insights from field deployments to inform product development and enhance our library of repeatable deployment strategies. - Manage and optimize deployment pipelines, model serving, and environment configurations in challenging, constrained settings. - Drive client adoption through effective documentation, training sessions, and smooth operational handovers. - Monitor and manage client satisfaction metrics such as CSAT, time-to-value, and uptime, proactively identifying and mitigating potential risks.

We are looking for individuals with:

- 3 to 6 years of experience in software or ML engineering, including at least one complete end-to-end on-premises or enterprise AI system deployment. - Proven production-grade expertise in Python, Docker, Linux system administration, REST APIs, and CI/CD practices. - Hands-on experience with LLM inference stacks like vLLM, TGI, or Ollama, along with RAG architectures and vector stores. - Demonstrated ability to deploy and manage systems in constrained environments, such as air-gapped networks, low-connectivity settings, or with non-standard hardware and regulatory requirements. - A strong full-stack debugging instinct, capable of diagnosing issues across infrastructure, networking, and application layers independently. - A track record of shipping and maintaining reliable, end-to-end working systems in high-stakes environments. - The capacity to interpret ambiguous client requirements and make decisive calls without constant explicit guidance.

Prior experience with strategic or complex enterprise accounts is a plus.