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 :
• 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 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

(ref : hirist.tech)