Key Responsibilities :

- Design, develop, and deploy scalable AI/ML and Generative AI solutions.

- Build and optimize RAG (Retrieval-Augmented Generation) pipelines using modern frameworks and vector databases.

- Develop LLM-powered applications leveraging prompt engineering, AI agents, and multi-agent workflows.

- Fine-tune, evaluate, and monitor machine learning and deep learning models.

- Build REST APIs and backend services for AI applications.

- Design data preprocessing, feature engineering, and model evaluation pipelines.

- Integrate structured and unstructured data sources to deliver contextual AI solutions.

- Collaborate with cross-functional teams including Data Engineering, DevOps, and Product teams.

- Ensure scalability, reliability, and performance of AI applications in production environments.

Tech Stack :

- Programming: Python, SQL.

- Frameworks: FastAPI, Scikit-learn, TensorFlow, PyTorch, Keras, LangChain, LangGraph.

- GenAI/LLM: Prompt Engineering, RAG Architectures, Agentic AI, OpenAI APIs, Hugging Face, LangSmith.

- Vector Databases: Pinecone, FAISS.

- Cloud & DevOps: AWS (EC2, S3, SageMaker, Bedrock), Docker, Git, JIRA, CI/CD.

Requirements :

- 2-5 years of overall experience.

- Minimum 1+ year of hands-on experience in GenAI/LLM projects.

- Strong understanding of software engineering best practices and Agile methodologies.