AI Developer

Role Overview

We are looking for an experienced AI/ML Engineer with strong software engineering expertise and hands-on experience in Generative AI, LLMs, agentic systems, and production AI applications. You will be responsible for designing scalable AI architectures, developing multi-agent workflows, building production-grade RAG systems, integrating enterprise tools and data, and optimizing AI applications for performance and reliability.

Responsibilities

• Design and develop multi-agent AI systems with planning, state management, tool usage, and self-correction capabilities.
• Build agentic workflows using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration.
• Implement MCP (Model Context Protocol) servers/clients, tool calling, and API integrations with enterprise systems.
• Develop scalable and low-latency RAG pipelines using vector databases, hybrid search, semantic caching, and re-ranking.
• Implement LLM evaluation, observability, tracing, and guardrails using tools such as Ragas and Langfuse.
• Develop high-performance asynchronous Python microservices and optimize latency, context usage, and inference performance.
• Explore and implement local model serving, fine-tuning, and quantization where appropriate.
• Mentor junior engineers and contribute to technical architecture and engineering best practices.

Requirements

• Bachelor's degree (Required)
• 4+ years of professional backend development in Python.
• Strong experience with FastAPI, asyncio, and microservice architectures.
• Hands-on experience with OpenAI, Anthropic Claude, Gemini, Llama, Mistral.
• Experience with LangGraph, AutoGen, CrewAI, or custom agent orchestration frameworks.
• Experience with Pinecone, Weaviate, Qdrant, Milvus, hybrid search, semantic retrieval, and re-ranking.
• Knowledge of relational and NoSQL databases and enterprise data integrations.
• Experience building or integrating MCP servers/clients, tool calling, and APIs.
• Experience with Docker, Kubernetes, AWS/GCP/Azure, and CI/CD pipelines.
• Exposure to monitoring, tracing, evaluation, guardrails, hallucination mitigation, and production AI observability.

Skills

• Python
• FastAPI
• LangGraph
• Pinecone
• Docker