Agentic AI Developer - Data Science

We are looking for a generative AI/Agentic AI Developer with strong experience in large language models (LLMs), AI agents, RAG architectures, and intelligent automation systems. The role involves designing, developing, and deploying next-generation AI-powered applications using modern GenAI frameworks and multi-agent architectures. The ideal candidate should have hands-on experience with LLM ecosystems, prompt engineering, AI orchestration frameworks, vector databases, and cloud-based AI deployments. The candidate will work closely with data engineering, product, and AI teams to build scalable AI-driven solutions for enterprise use cases.

Responsibilities:

• Design and develop GenAI and Agentic AI applications using LLMs.
• Build AI agents, multi-agent workflows, and autonomous task execution systems.
• Develop RAG-based pipelines using vector databases and retrieval frameworks.
• Integrate LLMs such as OpenAI, Claude, Gemini, Llama, or Mistral into enterprise applications.
• Work on prompt engineering, fine-tuning, evaluation, and response optimization.
• Build AI-powered assistants, Q& A systems, summarizers, automation bots, and intelligent workflows.
• Develop scalable APIs and backend services for AI applications.
• Collaborate with data engineering and product teams for AI solution deployment.
• Optimize model performance, latency, and cost efficiency.

Requirements:

• Strong proficiency in Python.
• Hands-on experience with LLMs and generative AI frameworks.
• Experience with LangChain, LangGraph, CrewAI, LlamaIndex, or similar frameworks.
• Strong understanding of RAG architecture and vector databases (Pinecone, ChromaDB, FAISS, Weaviate, etc. ).
• Experience with prompt engineering and LLM optimization.
• Exposure to multi-agent systems and agentic AI workflows.
• Knowledge of NLP, embeddings, semantic search, and AI orchestration.
• Experience with REST APIs, FastAPI, or backend integration
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Understanding of CI/CD, Docker, and deployment workflows