- Location
- Bengaluru, India
- Workplace
- Remote
- Department
- Engineering
- Seniority
- Senior
- Experience
- 1+ years
- Source
- Keka
Description
About the role
- We're hiring a Senior Backend Engineer to own high-throughput, real-time systems behind Convin's AI products: voicebots, AI agents, Auto QA and agent assist.
- Convin builds AI for contact centers. Our platform serves 100+ enterprise clients across BFSI, edtech and other sectors, handles 50M+ API requests a month, runs 500K+ voicebot calls a day and analyzes 500M+ conversations a year.
- You'll design systems end to end, ship them to production, and be on the hook for how they behave at scale. This is a role for an engineer who wants ownership, not tickets.
What you'll work on
- Real-time voice AI. Build and scale our Go voicebot across WebSocket and SIP, orchestrating STT → LLM → TTS with sub-second end-to-end latency at hundreds of thousands of calls a day.
- LLM systems in production. Design RAG pipelines, function calling (CRM updates, calendar booking, payment links) and multi-provider LLM orchestration with fallbacks, caching and guardrails against hallucination.
- Core platform services. Own high-volume services such as our rule engine, job scheduler and notification system, each running millions of operations a day.
- Scale and cost. Profile, tune and right-size services on Kubernetes; cut latency and cloud, LLM and TTS spend without hurting quality.
- Data layer. Model data and optimize queries across PostgreSQL, Redis, ClickHouse and Qdrant.
- Observability and reliability. Instrument services with Prometheus, Grafana and Jaeger; define SLOs, run on-call for what you own, and write blameless postmortems.
- Engineering leadership. Write design docs, review code, mentor SDE1/SDE2 engineers and interns, and raise the bar on code quality.
- Customers. Join technical discussions with enterprise clients when a design needs to fit their telephony, data or compliance constraints.
What we're looking for
- 3–5 years building backend systems, with at least 1 year owning services in production at meaningful scale.
- Strong in Go (preferred) or Python, with solid grasp of concurrency, networking and memory behavior.
- Hands-on distributed systems experience: queues (Kafka, RabbitMQ or Celery), caching, idempotency, retries, backpressure and graceful degradation.
- Production experience with Kubernetes and Docker on AWS or GCP, including autoscaling and resource tuning.
- Strong PostgreSQL skills: schema design, indexing, query plans; working knowledge of Redis.
- Has shipped at least one LLM-powered feature to real users and can talk about its latency, cost and failure modes.
- Uses metrics, logs and traces to debug, not guesswork.
- Writes clear design docs and can defend trade-offs with numbers.
- Ownership mindset: you've seen a system through launch, incidents and the rewrite after.
Nice to have
- Real-time audio or voice: WebRTC, SIP, telephony, or STT/TTS providers.
- RAG and vector databases (Qdrant or similar), embeddings, prompt engineering and LLM evaluation.
- ClickHouse, ScyllaDB or Elasticsearch for analytics or high-write workloads.
- Service mesh and infra tooling: Envoy, Istio, Terraform, Helm.
- gRPC and WebSocket services at high connection counts.
- Multi-tenant SaaS architecture, or single-to-multi-tenant migrations.
- Fluency with AI coding tools (Claude Code, Cursor, Copilot) in daily work.
- Startup experience, or having built something from zero to production.
Our stack
| Area | Tools |
| Languages | Go, Python, SQL |
| Services | REST, gRPC, WebSockets, WebRTC, SIP, Kafka, RabbitMQ, Celery |
| Infrastructure | Kubernetes (GKE, OCI), Docker, Envoy, Istio, Terraform, Helm |
| Data | PostgreSQL, Redis, ClickHouse, Qdrant, Elasticsearch |
| AI | OpenAI, Anthropic, Google, Qwen; multiple STT and TTS providers |
| Observability | Prometheus, Grafana, Jaeger, ELK |
Why Convin
- Real scale, early. Your code handles millions of conversations from day one, not a demo.
- Frontier problems. Sub-second voice AI, LLM cost and latency, and agentic systems that act on customers' behalf.
- Ownership. Small team, large surface area; you own systems, not tickets.
- Growth. Our backend leads grew from within, from intern to lead.
Hiring process
- Introductory call
- LLD / machine coding round
- HLD round
- Final interview with the hiring manager
Skills
PythonAWSGCPDockerKubernetesTerraformSQLPostgreSQLRedisElasticsearchRESTgRPCWebSocketCompliance