AI / ML & Generative AI
Develop and implement machine learning and NLP models for real-world applications.
Build Generative AI applications using LLMs such as OpenAI, Llama, and Mistral.
Design and optimize Retrieval-Augmented Generation (RAG) pipelines.
Work with embedding models, prompt engineering, and model fine-tuning.
Apply ML techniques like classification, regression, clustering, and dimensionality reduction.
Develop LLM workflows using LangChain and LangGraph.
Work on agent-based and multi-agent systems, including basic A2A communication.
Experiment with CrewAI and agent orchestration frameworks.
Backend & API Development
Build and maintain AI-backed APIs using FastAPI, Flask, or Django.
Integrate ML models into applications with scalable REST services.
Data & Databases
Work with data using Python, SQL, PostgreSQL, and ArangoDB.
Perform data processing and analysis using Pandas and NumPy.
Use vector databases such as FAISS, Milvus, or Chroma.
Deployment & MLOps
Containerize and deploy AI services using Docker.
Use Git and Linux for version control and development workflows.
Support on‑premise deployments and basic model monitoring.
Security & Access Control
Implement RBAC, authentication, and authorization using JWT and SSO.
Follow secure coding and data‑protection best practices.