GenAI Engineer

Core Responsibilities

Total IT experience 8 - 12 years. 2 – 3 years’ experience of developing AI solutions or machine learning engineering, or any related field. Proven experience taking AI/ML models from concept to production. Translate high-level client use cases and business problems into scalable, fully functional AI Solutions (traditional ML, GenAI, and RAG architectures). Architect and deploy autonomous and multi-agent AI systems capable of complex reasoning, decision-making, task routing, and external tool/API execution. Design, develop and deploy AI solutions using LLMs (OpenAI, Google Gemini, Claude) integrated with orchestration frameworks (LangChain, LlamaIndex) and Vector Databases. Write tailored prompts and construct advanced reasoning loops and chain-link components for autonomous workflows.

Required Technical Skills

Core Languages: Python (Required); Java and Angular/React (For full-stack UI/API integration of the solution). Agentic AI & Orchestration: Hands-on experience with agentic frameworks (e.g., LangGraph, CrewAI, AutoGen) and LLM function calling/tool usage. ML Frameworks: Hugging Face, LangChain, LlamaIndex, OpenAI API, TensorFlow, Keras, and PyTorch. Data & Vector Storage: Deep understanding of Vector Databases (e.g., Pinecone, ChromaDB) and RAG pipelines. AI Concepts: Agentic Workflows, Deep Learning. DevOps & MLOps : Containerization, cloud resource provisioning, and basic infrastructure-as-code, build CI/CD pipelines build, deploy, and monitor solutions