ROLE
– Develop and implement machine learning and NLP models for real-world applications.
– Build Generative AI applications using LLMs such as OpenAI, Llama, and Mistral.
– 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.
– Build and maintain AI-backed APIs using FastAPI, Flask, or Django.
– Integrate ML models into applications with scalable REST services.
– 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.
– 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.
– Implement RBAC, authentication, and authorization using JWT and SSO.
– Follow secure coding and data‑protection best practices.

REQUIREMENTS
– Programming: Python, SQL
– ML & AI: Scikit-learn, PyTorch or TensorFlow
– Generative AI: LLMs, RAG pipelines, prompt engineering
– Frameworks: LangChain, LangGraph, HuggingFace
– Backend: FastAPI / Flask / Django
– Databases: PostgreSQL, ArangoDB, Vector DBs
– Tools: Docker, Git, Linux
– Data & Visualization: Pandas, NumPy, Matplotlib, Power BI
– Bachelor’s degree in Computer Science, AI, Data Science, or related field
– 5–8 years of experience in AI/ML or Data Science roles