AI Engineer

Responsibilities:

• Building scalable backend services for AI applications.
• Integrating LLMs like OpenAI, Claude, Gemini, Bedrock, or vLLM into production.
• Building agentic AI workflows using LangGraph, LangChain, or similar frameworks.
• Designing asynchronous, event-driven systems using Kafka, RabbitMQ, WebSockets, or SSE.
• Handling retries, rate limiting, circuit breakers, and provider fallback when AI services fail.
• Optimising AI performance using Redis caching and low-latency architectures.
• Building microservices that support high-concurrency production traffic (10,000+ concurrent users).
• Deploying and monitoring applications on AWS with Kubernetes, Docker, and observability tools like Grafana or Prometheus.

Requirements:

• 3-5 years of backend engineering experience.
• Experience in Python (FastAPI/Django).
• Knowledge of production LLM integration (OpenAI, Claude, Gemini, Bedrock, vLLM, etc. ).
• Experience in agentic AI (LangGraph, LangChain, multi-agent systems).
• Familiar with async/event-driven architecture (Kafka, RabbitMQ, SQS, WebSockets, SSE).
• Know about Redis caching, microservices, high-concurrency, and low-latency systems.

Good to Have:

• Kubernetes, Docker, AWS.
• Observability (Grafana, Datadog, Prometheus).
• Fintech or payments experience.
• Startup experience.
• Experience breaking monoliths into microservices.