Sarvam
About this role
Sarvam is building India's sovereign AI stack, backed by Lightspeed, Peak XV and Khosla Ventures, and working with Tata Capital, SBI Life, CRED, IDFC and LIC. Strategic Deployment Engineers are its forward deployed technical staff. You embed with a client and own the full lifecycle of getting Sarvam's AI stack running in their environment, including air gapped, classified and on premises infrastructure where the standard playbook does not apply. You are the technical single point of contact for your accounts, from scoping and proof of concept through to steady state operations, and you diagnose integration failures, model drift, inference problems and infrastructure breakdowns yourself rather than escalating. The posting is blunt about how success is measured: whether the system works and the client trusts you, not tickets closed. At 3 to 6 years this is the lowest experience bar on today's board.
Who this is for
What the posting requires:
- 3 to 6 years in software or ML engineering, with at least one full cycle on premises or enterprise deployment delivered end to end.
- Production grade experience with Python, Docker, Linux systems administration, REST APIs and CI/CD pipelines.
- Hands on experience with LLM inference stacks such as vLLM, TGI or Ollama, plus RAG architectures and vector stores.
- Experience deploying in constrained environments: air gapped networks, limited connectivity, non standard hardware or complex regulatory requirements.
- Full stack debugging instinct, comfortable diagnosing across infrastructure, networking and application layers without a specialist to hand.
- Demonstrated ability to ship and maintain a working system end to end where reliability was non negotiable.
- Proven ability to navigate ambiguous client requirements and make the call without explicit guidance.
Nice to have:
- Prior experience with strategic or complex enterprise accounts.
- MCP server experience or familiarity with agentic frameworks.
- Open source projects, side products or entrepreneurial stints showing technical craft and sustained follow through.
What the work actually looks like:
- Own end to end deployment of Sarvam's full AI stack in client environments, including on premises, air gapped and classified infrastructure.
- Serve as technical single point of contact for assigned accounts, from scoping and proof of concept to steady state operations.
- Diagnose and resolve integration failures, model drift, inference issues and infrastructure breakdowns without escalation ladders.
- Manage deployment pipelines, model serving and environment configuration in constrained settings.
- Drive client side adoption through documentation, training and operational handover.
- Own client satisfaction, time to value and uptime for your accounts, and flag risks before they become escalations.
- Feed field learnings back into the product and the replicable deployment library.
What they say about the person: the posting asks for someone who treats ambiguity as the baseline, owns outcomes rather than tasks, and operates in forward deployed environments without daily oversight. It also says plainly that you are expected to be available and responsive when your clients are, which is worth weighing.
Location and working pattern: Delhi, embedded with client teams. No remote or hybrid arrangement is stated.
Honest fit guidance: this is the most accessible role on today's board by years, but it is not an easy one. The deciding requirement is the full cycle deployment you have already delivered, not the year count. If your experience is building features inside one company's own environment, the constrained deployment requirement will be the gap. No interview process is published.
- 3 to 6 years in software or ML engineering, with at least one full cycle on premises or enterprise deployment delivered end to end.
- Production grade experience with Python, Docker, Linux systems administration, REST APIs and CI/CD pipelines.
- Hands on experience with LLM inference stacks such as vLLM, TGI or Ollama, plus RAG architectures and vector stores.
- Experience deploying in constrained environments: air gapped networks, limited connectivity, non standard hardware or complex regulatory requirements.
- Full stack debugging instinct, comfortable diagnosing across infrastructure, networking and application layers without a specialist to hand.
- Demonstrated ability to ship and maintain a working system end to end where reliability was non negotiable.
- Proven ability to navigate ambiguous client requirements and make the call without explicit guidance.
Nice to have:
- Prior experience with strategic or complex enterprise accounts.
- MCP server experience or familiarity with agentic frameworks.
- Open source projects, side products or entrepreneurial stints showing technical craft and sustained follow through.
What the work actually looks like:
- Own end to end deployment of Sarvam's full AI stack in client environments, including on premises, air gapped and classified infrastructure.
- Serve as technical single point of contact for assigned accounts, from scoping and proof of concept to steady state operations.
- Diagnose and resolve integration failures, model drift, inference issues and infrastructure breakdowns without escalation ladders.
- Manage deployment pipelines, model serving and environment configuration in constrained settings.
- Drive client side adoption through documentation, training and operational handover.
- Own client satisfaction, time to value and uptime for your accounts, and flag risks before they become escalations.
- Feed field learnings back into the product and the replicable deployment library.
What they say about the person: the posting asks for someone who treats ambiguity as the baseline, owns outcomes rather than tasks, and operates in forward deployed environments without daily oversight. It also says plainly that you are expected to be available and responsive when your clients are, which is worth weighing.
Location and working pattern: Delhi, embedded with client teams. No remote or hybrid arrangement is stated.
Honest fit guidance: this is the most accessible role on today's board by years, but it is not an easy one. The deciding requirement is the full cycle deployment you have already delivered, not the year count. If your experience is building features inside one company's own environment, the constrained deployment requirement will be the gap. No interview process is published.
Apply on company site
Opens jobs.ashbyhq.com, the employer's own application page. Applying is always free.