About the Role
We're looking for a senior AI Platform Engineer (6+ years) who wants to take real ownership of our core backend platform and the AI agents running on top of it.
If you're someone who genuinely loves backend engineeringand has shipped production-grade agentic system - this role was written with you in mind.
You'll work closely with product, frontend, DevOps, and AI teams to shape both the foundation and the intelligent systems built on it.
Key Responsibilities
- Architect and lead development of scalable, high-performance backend services in Python
- Design and own REST API systems using FastAPI, including versioning, contract design, and lifecycle management
- Define and enforce authentication and authorization patterns (OAuth2, JWT, RBAC/ABAC)
- Architect and scale real-time systems using WebSockets, SSE, and event-driven patterns
- Own relational schema design, query optimization, and migration strategies in PostgreSQL
- Drive SQLAlchemy ORM standards and complex query patterns across the codebase
- Design solutions for caching, pub/sub, distributed queuing, and rate limiting
- Architect and own multi-agent orchestration systems — agent routing, tool-use pipelines, state management, and handoff logic
- Build reliable backend infrastructure for LLM-powered workflows — streaming, context management, retry logic, and failure recovery
- Implement durable workflow orchestration for agentic and data workloads
- Lead architectural discussions, technical design reviews, and cross-team alignment
- Define observability standards across both backend and AI layers — tracing, token usage, latency, alerting, and SLOs
- Help set a high bar for code quality, testability, and production-readiness
- Mentor mid-level and junior engineers; lead code reviews with constructive, actionable feedback
Required Skills & Qualifications
- 6+ years of professional backend development experience, primarily in Python
- A track record of building and scaling production backend systems with FastAPI or equivalent async Python frameworks
- Strong SQLAlchemy expertise — advanced patterns, performance tuning, and migration management
- Solid PostgreSQL skills: index design, query planning, partitioning, and schema ownership at scale
- Production experience with Redis across multiple use cases — caching, pub/sub, distributed locks, and queues
- Fluency in Python's async programming model — asyncio, concurrency patterns, and real-world pitfalls
- Hands-on experience designing and scaling real-time systems (WebSockets, SSE, or similar)
- Solid understanding of OAuth2 authentication flows, token lifecycle, and security best practices
- Production experience building agentic AI systems — multi-agent orchestration, LLM tool-use, dynamic routing, and conversation state management
- Familiarity with LLM APIs (OpenAI, Anthropic, or similar) — function calling, streaming, context window management, and cost control
- Experience with at least one agentic orchestration framework — LangGraph, LangChain, or equivalent
- Production experience with Temporal.io or similar for durable, distributed workflow execution
- Experience with LLM observability platforms — Langfuse, LangSmith, or similar — for tracing, evals, and cost tracking
- Familiarity with RAG pipelines — vector databases (pgvector, Pinecone, or similar), chunking strategies, and retrieval optimization
- Strong system design fundamentals — you've thought through and defended architectural decisions under real constraints
- Experience debugging complex production issues across both infrastructure and AI layers
- Solid Git hygiene and experience with mature CI/CD pipelines
Nice to Have
- Experience with voice AI pipelines — STT (Deepgram, Whisper), TTS, or real-time audio processing
- WebRTC or LiveKit experience for low-latency real-time communication
- Hands-on experience with Apache Airflow for data pipeline orchestration
- Deep OpenTelemetry integration — distributed tracing and custom metrics across AI and backend layers
- Kubernetes at an operational level — deployments, resource management, debugging pods
- Microservices architecture background — service boundaries, inter-service communication, failure isolation
- Experience in high-scale, multi-tenant SaaS environments
- Docker and container-native development practices
What We're Looking For
- We want someone who genuinely cares about both the backend foundation and the AI systems built on it.
- You're comfortable defining a technical approach from scratch.
- You've built structure and reliability around non-deterministic systems, and you know that comes with patience and good judgment, not just strong opinions.
- You're also a collaborator: you document your decisions, communicate clearly, and make the engineers around you better.
- You've been through production incidents on both infrastructure and AI layers, and you build systems designed to prevent them from happening again.
Note: This is work from office from Pune Location.
