Agentic AI Engineer

Key Responsibilities

• Build and deploy LLM-based workflows and AI agents for automation.
• Develop RAG (Retrieval-Augmented Generation) systems using vector databases and knowledge bases.
• Design Regex-based intent detection, classification, and entity extraction systems.
• Build LLM-powered intent classification and routing pipelines.
• Develop prompt engineering, prompt chaining, and structured output workflows.
• Integrate OpenAI / LLM APIs, embeddings, vector search, and AI tools.
• Build AI pipelines for text processing, classification, summarization, and generation.
• Evaluate and optimize LLM accuracy, latency, cost, and reliability.
• Deploy and monitor AI/ML models in production.

Must-Have Skills / Keywords

Python, Machine Learning, NLP, LLM, Generative AI, RAG, Retrieval-Augmented Generation, Regex, Intent Classification, Entity Extraction, Prompt Engineering, LLM Workflows, AI Agents, Vector Databases, Embeddings, Semantic Search, OpenAI API, LangChain, LangGraph, Transformers, Hugging Face, API Integration, FastAPI, Docker, Git, Model Evaluation, AI Automation.

Good to Have

Fine-tuning, LoRA/QLoRA, MLOps, AWS/GCP, MongoDB, PostgreSQL, Elasticsearch, Pinecone, Qdrant, FAISS, Redis, CI/CD, multi agent orchestration, cost and content optimisation.