Data Scientist - II
New
GurugramFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3-5 years
- Required Skills
- PythonGitMachine LearningPyTorchTensorflowRESTful APIsNLPLLMLangChain
Requirements
- 3-5 years of experience in data science, machine learning, and AI development with a strong focus on NLP and LLM applications.
- Bachelor's/Master's or higher degree in Computer Science, Machine Learning, Statistics, or related technical field.
- Proven track record of building and deploying production ML/AI systems from research to deployment.
- Mastery of Python with strong software engineering fundamentals (OOP, design patterns, testing).
- Deep hands-on experience with LLM frameworks and APIs (OpenAI, Anthropic, or similar).
- Strong experience with at least one deep learning framework (PyTorch or TensorFlow).
- Proficiency with modern ML orchestration and agentic frameworks (LangGraph, CrewAI, LangChain, or similar).
- Solid understanding of NLP techniques: embeddings, information extraction, semantic search, classification.
- Hands-on experience with advanced LLM features: tool calling, function calling, multi-turn conversations, structured outputs.
- Strong knowledge of software development practices: version control (Git), testing (pytest).
- Experience with REST APIs, async programming, and building scalable backend services.
- Familiarity with vector databases and embedding systems (Pinecone, Weaviate, FAISS, or similar).
Responsibilities
- Stay current with the latest LLM research, architectures, and advancements in the field including real-time models and multimodal systems.
- Design and build proof-of-concept solutions to validate technical feasibility using state-of-the-art LLMs.
- Develop robust, scalable agentic workflows using orchestration frameworks such as LangGraph or CrewAI.
- Implement advanced LLM features including tool calling, function calling, structured outputs, and multi-turn conversations.
- Build production-grade systems utilizing Model Context Protocol (MCP) and other emerging standards.
- Design and implement scalable, fault-tolerant architectures for real-time LLM-powered applications.
- Design rigorous experiments to test hypotheses, validate model performance, and develop evaluation frameworks.
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