Role Overview: We are looking for a hands-on AI Engineer with 2–3 years of experience to design, build, and deploy Generative AI and machine learning powered applications. The ideal candidate should be comfortable working with LLMs, embeddings, vector databases, and agent-based workflows and should have experience integrating AI models into production systems and enterprise workflows. This role is application-engineering focused (not pure research). You will be building real AI products such as chatbots, copilots, document intelligence systems, and knowledge assistants using modern GenAI frameworks. Key Responsibilities:  LLM & Generative AI Development  Build AI applications using LLMs (OpenAI, Gemini, Llama, Claude, or similar)  Implement RAG (Retrieval Augmented Generation) pipelines  Perform prompt engineering, prompt tuning, and evaluation of responses  Develop conversational AI assistants, copilots, and automation agents  Handle hallucination reduction, grounding, and response quality improvement AI Application Engineering:  Use LangChain / LangGraph / CrewAI or similar agent frameworks  Integrate LLMs with APIs, databases, and enterprise applications  Build document processing systems (PDF, emails, knowledge bases)  Develop multi-step reasoning workflows and AI agents  Implement tool-using agents (search, database querying, workflows) Data & Vector Infrastructure:  Create embeddings and semantic search pipelines  Work with vector databases such as: o Pinecone / FAISS / ChromaDB / PostgreSQL (pgvector)  Implement chunking strategies, indexing, and retrieval optimization  Build scalable ingestion pipelines Model Operations & Deployment:  Deploy models and applications via APIs and containers  Track and manage experiments using MLflow or similar  Monitor performance, latency, and response accuracy  Optimize token usage and inference cost  Implement evaluation metrics and feedback loops Required Skills:  Programming & Core Tech  Python (must have)  Strong understanding of APIs, REST services, JSON  Git and version control  Basic SQL & database querying AI/ML & GenAI:  Experience with LLM APIs (OpenAI / Azure OpenAI / Gemini / HuggingFace)  Prompt engineering and evaluation  Embeddings and semantic search  RAG architecture understanding  Familiarity with hallucination mitigation techniques Frameworks & Tools:  LangChain or LangGraph  Vector databases (FAISS / Pinecone / ChromaDB / pgvector)  Transformers / HuggingFace (basic usage)  FastAPI / Flask for serving models  Docker (basic level) Good to Have:  Knowledge of agent frameworks (CrewAI, AutoGen)  Web search grounding (Tavily or similar)  MCP (Model Context Protocol) usage  Basic cloud exposure (AWS / Azure / GCP)  Streamlit or Gradio UI apps  Fine-tuning or LoRA knowledge Educational Qualification:  B.Tech / BE / MS in Computer Science, AI, Data Science, or related field  Relevant certifications or GenAI coursework preferred