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Product Engineer

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Onsite

Product Engineer

Lyzr AI

Product Engineer

Skills

SQL QueriesMongoDBNextPythonGolangMicroservicesRedisAIAWSFastAPI

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

22 September 2026

What they need


Bangalore : On Site

Experience: 3-6 Years

About Lyzr Lyzr is revolutionizing enterprise AI adoption with its cutting-edge Agent API Studio and Universal Agents. As winners of the Accenture Ventures 2024 GenAI Challenge, Lyzr is leading the charge in AI-driven business transformation. Our solutions empower organizations to leverage intelligent automation and drive meaningful impact.

What You'll Do Build and maintain the core agent orchestration engine, skill execution, and state management. Develop the builder UI in Next.js. Implement real-time communication layers (WebSockets, SSE) for live agent interactions. Own backend microservices in Python and Go. Design and optimize MongoDB schemas for multi-tenant, high-throughput workloads. Manage cloud infrastructure, deployments, and observability on AWS.

What We Expect

  • Backend — Python (FastAPI) and Go microservices at scale. WebSocket and SSE implementation, diagnosing socket-level issues.
  • MongoDB at scale — sharding, indexing, replica sets. OAuth 2.0, JWT. Pub/Sub and event-driven architecture.
  • Queue management, state persistence, retry and dead-letter handling. Latency, networking, and distributed systems performance.
  • Frontend — Next.js. CSR vs SSR trade-offs. Hydration debugging. State management across client and server boundaries. Scalable component architecture. Reverse proxying and build optimization.
  • Infrastructure — ECS, CloudWatch, VPCs, load balancers, security groups, DocumentDB, Redis, SigNoz or similar observability tooling. -
  • System Design — LLD (design patterns, SOLID, API contracts), HLD (distributed architecture, service decomposition, scalability, fault tolerance), database design (SQL & NoSQL schema modeling, indexing, query optimization, consistency trade-offs).

Nice to have

  • AI agent frameworks
  • LLM integration patterns (prompt chaining, function calling, structured outputs)
  • Firecracker MicroVMs or E2B-style sandboxed execution
  • agent memory and long-running task orchestration.

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