Description

Role Overview

We are looking for a hands-on AI / GenAI Engineer to construct, optimize, and deploy production-ready Generative AI features and multi-agent workflows. In this role, you will implement complex Agentic patterns, integrate RAG pipelines with enterprise data stores, and build resilient microservices around LLM architectures.

Key Responsibilities

• Agent & Workflow Implementation: Develop, test, and iterate on complex agentic workflows (tool execution, self-reflection, planning, multi-agent collaboration) using frameworks like LangGraph, AutoGen, CrewAI, or LlamaIndex.
• Pipeline & RAG Development: Build and maintain high-performance Retrieval-Augmented Generation (RAG) systems, implementing hybrid search, re-ranking, and vector database integrations.
• API & System Integration: Expose LLM applications via clean, low-latency REST/gRPC APIs (e.g., FastAPI, Node.js) and integrate them into existing backend microservices and databases.
• Model Optimization & Prompting: Design advanced prompt chains, evaluate open-source/proprietary LLMs, and optimize for latency, token consumption, and model routing.
• Testing & Monitoring: Implement continuous evaluation pipelines (RAGAS, TruLens) and operational monitoring (LangSmith, Phoenix, Datadog) to track hallucinations, latency, and drift.

Qualifications & Technical Skills

• Experience: 3+ years of backend software engineering experience, with 1.5+ years of hands-on production experience building Generative AI applications and autonomous agents.
• Programming: Strong proficiency in Python or TypeScript with solid experience in asynchronous programming and microservice design.
• Agentic Frameworks & AI Tools: Hands-on experience with LangChain, LangGraph, LlamaIndex, or AutoGen, along with function-calling/tool-use APIs.
• Databases & Vector Search: Hands-on work with vector databases (Pinecone, Qdrant, Milvus, Weaviate, Pgvector) and traditional relational/NoSQL stores.
• Cloud & Infrastructure: Experience deploying containerized applications (Docker, Kubernetes) across major cloud platforms (AWS, Azure, GCP).

Key Skills

Education

Any Graduate