Data Scientist

Role & 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 workflowsWork with vector databases (e.g., Pinecone) to enable semantic search and retrievalDesign and maintain ETL pipelines and ensure smooth data preprocessing and transformationImplement NLP solutions for tasks like intent detection, sentiment analysis, and content generationDevelop backend APIs and services using Python frameworks like FastAPI or FlaskContribute 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 systemMachine Learning Fundamentals: Strong grasp of algorithms, model training, evaluation, and tuningGenerative AI Models: Experience working with LLMs, RAG architecture, and fine-tuning techniquesLangChain or Similar Frameworks: Hands-on experience building AI workflows using toolkits like LangChainNatural Language Processing (NLP): Proficiency in text analytics, classification, tokenization, embeddingsVector Databases: Practical use of tools like Pinecone, FAISS, or similar for retrieval-augmented generationBig Data Handling: Ability to work with large datasets, optimize storage, and processing pipelinesSQL/NoSQL: Experience in querying and managing structured and unstructured dataPython & API Development: Proficiency in Python and frameworks like FastAPI or FlaskETL & Data Preprocessing: Strong understanding of building pipelines for clean and efficient data processingSoft Skills: Strong problem-solving, communication, and collaboration abilitiesGood-to-Have Skills:Agentic AI Tools: Exposure to CrewAI or similar platforms for orchestrating multi-agent interactionsContent Structuring: Experience in clustering, topic modeling, or organizing unstructured dataETL Enhancements: Advanced optimization techniques for faster and more efficient pipelinesDomain Exposure: Prior work on projects involving customer insights, chat summarization, or sentiment analysis