Principal / Sr Principal Engineer, AI/Computer Vision and LLMs (MTS-4/5) (copy)

Key Responsibilities:

• Technical Leadership & Architecture: Lead the design and architecture of scalable and robust AI/ML systems, particularly focusing on computer vision and LLM applications for sports media analysis.
• Model Development & Training: Spearhead the development, training, and fine-tuning of sophisticated deep learning models (e.g., object detectors like RT-DETR, custom classifiers, generative models) on large-scale, domain-specific datasets (like sports imagery and video).
• Generalized Object Detection: Develop and implement advanced computer vision models capable of identifying a wide array of visual elements (e.g., logos, brand assets, on-screen graphics) in diverse and challenging sports content, including those not seen during training.
• LLM & GenAI Integration: Explore and implement solutions leveraging LLMs and Generative AI for tasks such as content summarization, insight generation, data augmentation, and model validation (e.g., using vision models to verify detections).
• System Implementation & Deployment: Build and deploy production-ready AI/ML pipelines, ensuring efficiency, scalability, and maintainability. This includes developing APIs and integrating models into broader Nielsen Sports platforms.
• UI/UX for AI Tools: Guide or contribute to the development of internal tools and simple user interfaces (using frameworks like Streamlit, Gradio, or web stacks) to showcase model capabilities, facilitate data annotation, and allow for human-in-the-loop validation.
• Research & Innovation: Stay at the forefront of advancements in computer vision, LLMs, and related AI fields. Evaluate and prototype new technologies and methodologies to drive innovation within Nielsen Sports.
• Mentorship & Collaboration: Mentor junior engineers, share knowledge, and collaborate effectively with cross-functional teams including product managers, data scientists, and operations.
• Performance Optimization: Optimize model performance for speed and accuracy, and ensure efficient use of computational resources (including cloud platforms like AWS, GCP, or Azure).
• Data Strategy: Contribute to data acquisition, preprocessing, and augmentation strategies to enhance model performance and generalization.

Required Qualifications:

• Bachelors of Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• 5+ years (for Principal / MTS-4) / 8+ years (for Senior Principal / MTS-5) of hands-on experience in developing and deploying AI/ML models, with a strong focus on Computer Vision.
• Proven experience in training deep learning models for object detection (e.g., YOLO, Faster R-CNN, DETR variants like RT-DETR) on custom datasets.
• Experience in finetuning LLMs like Llama 2/3, Mistral, or open-source models available on Hugging Face using libraries such as Hugging Face Transformers, PEFT, or specialized frameworks like Axolotl/Unsloth.
• Proficiency in Python and deep learning frameworks such as PyTorch (preferred) or TensorFlow/Keras.
• Demonstrable experience with Multi Modal Large Language Models (LLMs) and their application, including familiarity with transformer architectures and fine-tuning techniques.
• Experience with developing simple UIs for model interaction or data annotation (e.g., using Streamlit, Gradio, Flask/Django).
• Solid understanding of MLOps principles and experience with tools for model deployment, monitoring, and lifecycle management (e.g., Docker, Kubernetes, Kubeflow, MLflow).
• Strong software engineering fundamentals, including code versioning (Git), testing, and CI/CD practices.
• Excellent problem-solving skills and the ability to work with complex, large-scale datasets.
• Strong communication and collaboration skills, with the ability to convey complex technical concepts to diverse audiences.
• Full Stack Development experience in any one stack

Preferred Qualifications / Bonus Skills:

• Experience with Generative AI vision models for tasks like image analysis, description, or validation.
• Track record of publications in top-tier AI/ML/CV conferences or journals.
• Experience working with sports data (broadcast feeds, social media imagery, sponsorship analytics).
• Proficiency in cloud computing platforms (AWS, GCP, Azure) and their AI/ML services.
• Experience with video processing and analysis techniques.
• Familiarity with data pipeline and distributed computing tools (e.g., Apache Spark, Kafka).
• Demonstrated ability to lead technical projects and mentor team members.