About the role
Key Responsibilities:
- 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.
Required Skills:
- 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
Education:
- Bachelor’s degree in Computer Science, AI, Data Science, or related field
- 5–8 years of experience in AI/ML or Data Science roles