Data Scientist (NLP/LLM)

Requirements

• Professional Experience: 1.5 to 3 years of hands-on professional experience in a Data Science, Machine Learning, or AI/Software Engineering role, with significant focus on Generative AI or related NLP/Search technologies in the last 12-18 months
• Programming Proficiency: Python
• GenAI Stack Experience: Hands-on experience with at least one major orchestration framework (e.g., LangChain, LlamaIndex)
• Search & Retrieval Expertise: Practical experience implementing vector databases, creating embeddings, and designing indexing and chunking strategies for RAG systems
• Data Fluency: Solid understanding of SQL and data modeling; practical experience handling and transforming unstructured data
• LLMOps Fundamentals: Familiarity with the principles and tooling for experiment tracking, version control for prompts/pipelines, and tracing/observability in an LLM context
• Demonstrated Ability: A strong portfolio or track record showing the successful implementation (in production or advanced project settings) of Generative AI components
• Upper-Intermediate level of English

WOULD BE A PLUS:

• Experience or deep interest in agentic design patterns and multi-step reasoning
• Familiarity with foundational MLOps tools for CI/CD and production deployment
• Familiarity with coding assistants like Cursor, Copilot, Claude Code, Windsurf, etc.
• Exposure to multimodal applications (text/image/video/audio)
• Academic background or practical experience in core Machine Learning and Deep Learning concepts

Responsibilities

• Translate business requirements into functional AI systems (e.g., intelligent assistants, copilots, simple autonomous agents) with defined quality and performance metrics
• Build and implement cutting-edge RAG systems (“RAG 2.0”), focusing on:
  • Implementing hybrid retrieval (vector, keyword) and structured data retrieval methods
  • Designing and testing effective chunking strategies and embedding models
  • Implementing memory and conversational history management for agents
• Develop effective prompt engineering techniques and data pipelines to efficiently utilize long-context models for document-heavy use cases
• Contribute to the evaluation and selection of models (proprietary APIs vs. open-weight models) based on performance, cost, and deployment requirements
• Implement production elements of the GenAI stack: data handling, prompt orchestration (e.g., using frameworks), tracing, and caching for performance