Member of technical staff - 3 - ML + Gen AI

Are you passionate about building innovative solutions with the latest advancements in data science and Large Language ModelsDo you want to contribute to industry-defining software and grow your skills at the cutting edge of AI research and applicationAt Nielsen Sports, we provide the most comprehensive and trusted data and analytics for the global sports ecosystem. In this role, you will be a key contributor to our mission, developing and implementing AI systems that unlock novel insights from complex sports data, from match statistics to fan commentary. We are looking for an enthusiastic Software Engineer (MTS-3) to join our dynamic team.Qualifications

• Key Responsibilities

• AI/ML System Development: Contribute to the design, development, and implementation of scalable AI/ML systems, with a focus on both LLM applications and classical ML models for sports analytics.

• Model Implementation & Fine-Tuning: Implement, train, and test machine learning models (e.g., classifiers, regression models using libraries like Scikit-learn) and fine-tune LLMs using frameworks like PEFT.

• LLM & Agent Development: Design and build LLM-powered applications, including RAG (Retrieval-Augmented Generation) systems, chatbots, and autonomous agents using frameworks like LangChain, LangFlow, or LlamaIndex.

• Data Science & Analytics: Apply foundational data science techniques to explore and extract insights from structured and unstructured sports data, supporting business decisions and feature engineering.

• Software Development & Deployment: Write high-quality, maintainable Python code for AI/ML pipelines. Contribute to building APIs and integrating models into Nielsen Sports platforms.

• UI/UX for AI Tools: Develop or assist in developing internal tools and simple user interfaces (using frameworks like Streamlit or Gradio) to showcase model capabilities and support data workflows.

• Required Qualifications

• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field.

• 3-6 years of hands-on software development experience with a strong focus on both applied Machine Learning and LLM application development.

• Proficiency in Python and experience with ML/DL frameworks such as PyTorch, TensorFlow/Keras, and Scikit-learn.

• Experience building applications with LLMs via APIs (e.g., OpenAI, Anthropic) or open-source models (e.g., Llama, Mistral).

• Hands-on experience with LLM frameworks like LangChain, LangFlow, or LlamaIndex for building agents and RAG pipelines.

• Solid understanding of MLOps principles and familiarity with tools like Docker and Git.

• Strong software engineering fundamentals, including data structures, algorithms, and testing.