Strategic Deployment Engineer, Chanakya

About this job

Sarvam AI is developing India's full-stack sovereign AI platform, spanning research, models, infrastructure, and applications to make artificial intelligence work for the country. As a Strategic Deployment Engineer for Chanakya at Sarvam in Delhi, you will act as a forward-deployed technical asset embedded directly with leading enterprises and public institutions. In this role, you will own the complete lifecycle of AI system rollouts, particularly within complex, air-gapped, and on-premise environments. A normal day involves serving as the primary technical point of contact for assigned accounts, managing deployment pipelines, configuring models, troubleshooting infrastructure issues, and driving client adoption. You will operate with a high degree of autonomy, ensuring that deployed systems are reliable, building deep client trust, and feeding field insights back into the product development layer to create durable capabilities for India.

Responsibilities

• Manage end-to-end deployment of Sarvam's AI stack in client environments including on-prem and air-gapped infrastructure.
• Act as the technical single point of contact for assigned enterprise accounts from scoping through to steady-state operations.
• Troubleshoot and fix integration failures, model drift, inference problems, and infrastructure breakdowns independently.
• Feed field-level insights back into the core product layer and replicable deployment libraries.
• Oversee deployment pipelines, model serving, and environment configuration in constrained settings.
• Drive client adoption through comprehensive documentation, training, and operational handovers.
• Manage account outcomes such as client satisfaction, time-to-value, and uptime while proactively flagging risks.

Requirements

• 3 to 6 years of software or machine learning engineering experience with at least one complete on-prem or enterprise deployment.
• Production-level expertise in Python, Docker, Linux systems administration, REST APIs, and CI/CD pipelines.
• Hands-on background working with LLM inference stacks like vLLM, TGI, or Ollama, along with RAG architectures and vector stores.
• Experience deploying technology in constrained environments such as air-gapped networks, limited connectivity, or strict regulatory settings.
• Strong full-stack debugging skills across infrastructure, networking, and application layers.
• Proven track record of shipping and maintaining reliable working systems end-to-end.
• Ability to handle ambiguous client requirements and make independent decisions without explicit guidance.

Good to have

• Prior experience working with strategic or complex enterprise accounts.
• Familiarity with Model Context Protocol (MCP) servers or agentic frameworks.
• Open-source projects, side products, or entrepreneurial experience demonstrating technical craft.

Who should apply

This role is well-suited for autonomous software or machine learning engineers who thrive in high-pressure, forward-deployed environments without daily oversight. If you are comfortable treating ambiguity as a baseline, taking full ownership of outcomes rather than just tasks, and solving difficult integration problems directly with clients, you will fit right in. Candidates must bring solid production experience in Python and infrastructure, along with a strong bias for action. The posting does not mention if freshers can apply, as it specifically requires 3 to 6 years of prior professional engineering experience.

Frequently asked questions