• GenAI & LLM Frameworks
• Vector Databases (e.g., AWS OpenSearch & Kendra, FAISS, Pinecone)
• Word and Vector Embeddings (e.g., Word2Vec, BERT, Sentence Transformers)
• Frameworks & Toolkits (AWS Bedrock, LangChain, LangGraph, LlamaIndex)
• Cloud - AWS (preferably), Azure
• Agentic AI & Automation - AWS Bedrock Agents, LangGraph, AutoGen, CrewAI
• Programming & ML Skills
• Python (mandatory), SQL (Postgres), NoSQL (Dynamo DB, Mongo DB), Graph DB (Neo4j, Neptune)
• Libraries numpy, pandas, boto3, plotly, matplotlib, seaborn, ggplot, Scikit-learn, Requests, Beautiful Soup, NLTK)
• ML algorithms (Supervised, Unsupervised and Ensemble) & Deep Learning (using frameworks like TensorFlow, PyTorch, etc.)
• Web Development frameworks (Django, Flask, FastAPI)
• Conversational AI
• Chatbot development using LLMs or traditional NLP pipelines
• UI development using Python Streamlit or Gradio libraries

Roles & Responsibilities

1. Design, develop, and fine-tune AI models using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
2. Implement and optimize large language model (LLM) workflows using LangChain, LangGraph, and LlamaIndex.
3. Develop and orchestrate intelligent agents and agentic workflows for task automation.
4. Integrate vector databases and embedding techniques (word/vector embeddings) for semantic search and knowledge retrieval.
5. Build, test, and deploy chatbots and AI systems tailored to business requirements.

Apply machine learning & Deep Learning models for classification, segmentation, and regression problems Integration with platforms and APIs

Collaborate with cross-functional teams to deliver production-grade AI applications.