Data Scientist

Mandatory Skills:

CrewAI / Agentic AI Generative AI (LLMs RAG Fine-tuning), LangChain, Microservices, Natural Language Processing (NLP), Open Source LLM, Pandas, Python, Topic Modeling / Clustering, Vector Databases (e.g. Pinecone), AI Voice Agent

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 and integrate AI Voice Agents capable of handling natural, conversational interactions including voice input/output, real-time speech synthesis, and intent recognition using LLM-based frameworks
• 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.
• 3 to 6 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
• AI Voice Agent Development: Hands-on experience with speech-to-text (ASR), text-to-speech (TTS), and conversational voice interfaces leveraging frameworks like OpenAI Whisper, SpeechBrain, or AWS Lex/Polly.