Generative AI Engineer - Data Science

Roles and Responsibilities :

- Develop and fine-tune LLMs using techniques like RAG, transfer learning, or domain-specific adaptation

- Build AI agents using frameworks like LangChain or CrewAI to manage dynamic and multi-step workflows

- Work with vector databases (e.g., Pinecone) to enable semantic search and retrieval

- Design and maintain ETL pipelines and ensure smooth data preprocessing and transformation

- Implement NLP solutions for tasks like intent detection, sentiment analysis, and content generation

- Develop backend APIs and services using Python frameworks like FastAPI or Flask

- Contribute to scalable microservice-based architectures

Requirements :

- Bachelor's degree in Computer Science, Information Technology, or a related field.

- 2 to 4 years in AI/ML development and backend system

- Machine Learning Fundamentals : Strong grasp of algorithms, model training, evaluation, and tuning

- Generative AI Models : Experience working with LLMs, RAG architecture, and fine tuning techniques

- LangChain or Similar Frameworks : Hands-on experience building AI workflows using toolkits like LangChain.

- Natural Language Processing (NLP) : Proficiency in text analytics, classification, tokenization, embeddings.

- Vector Databases : Practical use of tools like Pinecone, FAISS, or similar for retrieval-augmented generation

- Big Data Handling : Ability to work with large datasets, optimize storage, and processing pipelines

- SQL/NoSQL : Experience in querying and managing structured and unstructured data

- Python & API Development : Proficiency in Python and frameworks like FastAPI or Flask

- ETL & Data Preprocessing : Strong understanding of building pipelines for clean and efficient data processing

- Soft Skills : Strong problem-solving, communication, and collaboration abilities.

Good-to-Have Skills :

- Agentic AI Tools : Exposure to CrewAI or similar platforms for orchestrating multi agent interactions.

- Content Structuring : Experience in clustering, topic modeling, or organizing unstructured data

- ETL Enhancements : Advanced optimization techniques for faster and more efficient pipelines

- Domain Exposure : Prior work on projects involving customer insights, chat summarization, or sentiment analysis