Turn loosely defined goals into RFCs, milestones and shipped results, and write down the decisions and trade-offs.
Be accountable after launch: on-call, incident response, root-cause analysis and follow-through on fixes.
Make the call when a decision is needed, and own the outcome.
Obsessed with latencies, dissecting existing services to understand the system, and tuning the right knobs to optimize performance.
Bring services across without regressions in customer-facing APIs, latency or reliability.
Build real-time and model-serving pipelines
Build and operate streaming infrastructure: WebSocket connections, long-lived sessions queue-based batch processing, backpressure and low-latency model invocation.
Engineer audio pipelines: TTS chunking around model context limits, sample-rate conversion and format encoding, and VAD-based silence detection for ASR.
Hold your services to sub-100ms latency SLOs in a multi-tenant environment, and know where every millisecond goes.
Think in systems and from first principles
Reasons from constraints: latency budgets per hop, throughput, cost per request, failure modes and tenant isolation.
Use back-of-the-envelope estimates to decide what to build, what to optimize and what to leave alone.
Prefer simple designs you can fully explain over clever ones you cannot.
Build in observability from day one: logging, metrics and tracing that let anyone debug production quickly.
Raise the bar for the team
Strengthen the integration testing harness so the team can ship fast without breaking customer-facing APIs.
Partner with the Inference and MLOps teams on making model serving fast, reliable and operable.
Mentor junior engineers through RFC reviews, code review and pairing.
Practice design-first, documentation-first engineering: RFCs before code, no tribal knowledge.
What we are looking for
4–6 years of building and operating production backend systems.
At least one service you owned end to end under real load, including what broke and what you changed.
Strong distributed-systems fundamentals: concurrency, networking, queues, caching, consistency and failure modes.
Experience with low-latency or high-throughput services, and comfort reasoning about p95 and p99 latency.
Solid fundamentals with a relational database and a cache, such as PostgreSQL and Redis: schema design, query performance and caching strategy.
Fluency in at least one backend language and willingness to work in both Python and Golang.
Experience running services with containers, CI/CD and production monitoring.
Daily, hands-on use of AI tools in your engineering workflow, with concrete examples of what you automated or sped up.
A bias for action: fast on reversible decisions, careful on irreversible ones.
Clear writing, especially RFCs and design docs that others can act on.
Good to have (not required)
Voice AI or real-time audio experience: WebSockets, WebRTC, streaming ASR or TTS, or audio processing with FFmpeg or pyav, codecs, sample rates and VAD.
Production experience in Go, or in Python with FastAPI.
Experience serving LLM, ASR, TTS or vision models in production.
Experience building metering, billing, wallet or payments infrastructure.
Running services on Kubernetes in production.
Time at an early- or growth-stage startup.
Why Sarvam
A fast-moving, high talent-density team building full-stack AI for India, with real population-scale impact.
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.
AI-first in everything, from how we build and ship to how we think about problems
