Strategic Deployment Engineer, Chanakya
Sarvam AI
Delhi, IndiaPosted 4 months ago
S
Skill Required
EngineeringCD pipelinesnetworkingdebuggingbuildingEmbedded CPythonDockerLinuxCI/CDAPIsRAGGenerative AICICDAIMachine LearningFulltime
Key highlights
- Required experience: 3–6 years in software or ML engineering with full‑cycle on‑prem or enterprise deployment.
- Must have production‑grade Python, Docker, Linux, REST API, and CI/CD pipeline experience.
- Work involves deployments in air‑gapped, classified, and other constrained environments.
- High ownership and high impact from day one.
- Fast‑moving, high talent‑density team backed by top venture capital firms.
- Opportunity to affect AI adoption at a country‑scale level.
Role overview
Strategic Deployment Engineers at Sarvam are forward‑deployed technical assets embedded with client organizations, responsible for end‑to‑end deployment, operation, and maintenance of Sarvam’s full AI stack across on‑prem, air‑gapped, classified, and complex enterprise environments. They act as the technical single point of contact, ensuring system reliability, client satisfaction, and durable capability while operating with autonomy and full accountability.
Responsibilities
- Own end-to-end deployment of Sarvam's full AI stack in client environments — on-prem, air-gapped, classified infrastructure, and complex enterprise accounts
- Serve as technical SPOC for assigned accounts, from scoping and PoC through to steady-state operations
- Diagnose and resolve integration failures, model drift, inference issues, and infrastructure breakdowns without escalation ladders
- Surface field learnings that feed back into the product layer and replicable deployment library
- Manage deployment pipelines, model serving, and environment configuration in non-standard, constrained settings
- Drive client-side adoption through documentation, training, and operational handover where required
- Own client satisfaction (CSAT, time-to-value, uptime) for your accounts; flag risks before they become escalations
Requirements
- 3–6 years in software or ML engineering, with at least one full-cycle on-prem or enterprise deployment delivered end-to-end
- Production-grade experience in Python, Docker, Linux systems administration, REST APIs, and CI/CD pipelines
- Hands-on experience with LLM inference stacks — vLLM, TGI, Ollama, or equivalent — and 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 in environments where reliability was non-negotiable
- Proven ability to navigate ambiguous client requirements and make the call without explicit guidance
- You've shipped and maintained a working system end-to-end in environments where the bar for reliability was non-negotiable
- You've navigated ambiguous client requirements and made 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 that demonstrate technical craft and sustained follow-through
Benefits
- Work alongside researchers, engineers, builders, and business leaders who move fast and hold each other to a very high bar
- High ownership and high impact, from day one
- Everything we do is AI‑first, from the way we build and ship to the way we think about problems
- Opportunity to work on problems that could change how an entire country learns, works, and communicates
- Fast‑moving, high talent‑density team building full‑stack AI for India
- Exposure to leading enterprises and public institutions, and backing from Lightspeed, Peak XV, and Khosla Ventures
Additional details
- Sarvam is building the bedrock of Sovereign AI for India, developing a full‑stack sovereign AI platform across research, models, infrastructure and applications with a focus on making AI work for India.
- Sarvam works with leading enterprises and public institutions and is backed by Lightspeed, Peak XV, and Khosla Ventures.
- Partners include Tata Capital, SBI Life, CRED, IDFC, and LIC.
- Strategic Deployment Engineers are forward‑deployed technical assets embedded with clients, owning the full lifecycle of AI system deployments in air‑gapped, classified, and on‑prem environments and in complex enterprise accounts where standard playbooks don’t apply.
- Success is measured by system functionality, client trust, and durable capability rather than ticket closure.
- The role operates with autonomy and real accountability for the system, the relationship, and outcomes.
- Who You Are: You treat ambiguity as the baseline — you don't need perfect information to move; you own outcomes, not tasks; you operate in high‑pressure, forward‑deployed environments without daily oversight; you are available and responsive when your clients are; you have a strong bias for action and openness to being wrong; you are intellectually restless — the hardest, least‑understood problems energise you.