Netomi - Data Scientist
Requirements
• Research & Innovation - Stay current with the latest LLM research, architectures, and advancements in the field including real-time models and multimodal systems. Evaluate emerging techniques and methodologies for potential application to business problems. Monitor developments in transformer architectures, fine-tuning approaches, model optimization, and real-time inference. Research and assess new LLM capabilities, frameworks, and API features as they emerge • Solution Design & Prototyping - Identify and define approaches for complex AI challenges leveraging state-of-the-art LLMs. Design and build proof-of-concept solutions to validate technical feasibility. Rapidly prototype LLM-based applications using modern frameworks and orchestration tools. Conduct rigorous experiments to evaluate different approaches and methodologies. Work collaboratively in multi-disciplinary team environments and establish professional networks with subject matter experts • Production Development & Software Engineering - Write clean, maintainable, production-quality code following software engineering best practices and design patterns. Develop robust, scalable agentic workflows using orchestration frameworks (such as LangGraph, CrewAI, or similar). 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. Conduct thorough code reviews and maintain high code quality standards. Optimize code for performance, memory efficiency, and cost-effectiveness in production environments • Experimentation & Optimization - Design rigorous experiments to test hypotheses and validate model performance. Develop evaluation frameworks for LLM outputs, system performance, and user experience. Optimize prompt engineering strategies, fine-tuning approaches, and inference efficiency. Conduct A/B tests, performance benchmarking, and statistical analysis • 3-5 years of experience in data science, machine learning, and AI development with 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 • Experience with diverse ML models: neural networks, transformers, SVM, Random Forest, clustering, Bayesian models • 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) • Knowledge of distributed computing, cloud platforms (AWS, GCP, or Azure), and containerization (Docker) • Strong experimental design skills with ability to formulate hypotheses and conduct rigorous analysis • Excellent problem-solving abilities and intellectual curiosity to stay current with AI research • Self-motivated with proven ability to work collaboratively in multi-disciplinary teams • Experience with voice/speech models and real-time audio processing (OpenAI Realtime API or similar) • Knowledge of Model Context Protocol (MCP) and emerging LLM standards • Experience with MLOps tools and practices (model monitoring, versioning, A/B testing) • Contributions to open-source ML/AI projects or published research papers • Familiarity with streaming architectures and event-driven systems (Kafka, RabbitMQ)
Responsibilities
• Research & Innovation - Stay current with the latest LLM research, architectures, and advancements in the field including real-time models and multimodal systems. Evaluate emerging techniques and methodologies for potential application to business problems. Monitor developments in transformer architectures, fine-tuning approaches, model optimization, and real-time inference. Research and assess new LLM capabilities, frameworks, and API features as they emerge • Solution Design & Prototyping - Identify and define approaches for complex AI challenges leveraging state-of-the-art LLMs. Design and build proof-of-concept solutions to validate technical feasibility. Rapidly prototype LLM-based applications using modern frameworks and orchestration tools. Conduct rigorous experiments to evaluate different approaches and methodologies. Work collaboratively in multi-disciplinary team environments and establish professional networks with subject matter experts • Production Development & Software Engineering - Write clean, maintainable, production-quality code following software engineering best practices and design patterns. Develop robust, scalable agentic workflows using orchestration frameworks (such as LangGraph, CrewAI, or similar). 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. Conduct thorough code reviews and maintain high code quality standards. Optimize code for performance, memory efficiency, and cost-effectiveness in production environments • Experimentation & Optimization - Design rigorous experiments to test hypotheses and validate model performance. Develop evaluation frameworks for LLM outputs, system performance, and user experience. Optimize prompt engineering strategies, fine-tuning approaches, and inference efficiency. Conduct A/B tests, performance benchmarking, and statistical analysis
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