Summary:

The AI/ML Engineer will design, develop, deploy, and maintain machine learning models and analytical algorithms that support athlete performance analytics, injury prediction, talent identification, event insights, and data-driven decision-making under the Sports Digital Ecosystem. The role includes working with structured/unstructured sports datasets, integrating ML pipelines with backend systems, and ensuring alignment with security, compliance, and performance standards. The resource will work closely with data, backend, DevOps, and domain teams to deliver AI-driven capabilities for dashboards, monitoring tools, and advanced analytics.

Key responsibilities :

Design, build, train, and evaluate ML models for prediction, classification, clustering, and recommendations. Develop data pipelines for ingestion, preprocessing, feature engineering, and model deployment. Implement use cases such as athlete performance analysis, injury risk prediction, talent identification, event analytics, and anomaly detection. Work with large datasets from federations, training centers, IoT devices, assessments, and historical records. Deploy ML models using APIs, microservices, and container-based infrastructure. Tune models, optimize performance, and conduct A/B testing for algorithm refinement. Implement MLOps practices for monitoring, retraining, versioning, and CI/CD integration. Collaborate with data engineers, backend developers,



and UI/UX teams to integrate ML outputs into dashboards and workflows. Prepare documentation for ML models, datasets, experiments, and integration guidelines. Ensure data security, ethical AI use, and compliance with government data policies.

Required Skills and Qualifications:

Bachelors degree in Engineering, Computer Science, Data Science, AI/ML, or related field. Solid understanding of machine learning algorithms, statistics, probability, and data modeling. Hands-on experience with Python, NumPy, Pandas, Scikit-learn, and ML frameworks such as TensorFlow or PyTorch. Experience building ML pipelines, feature engineering workflows, and ETL data processing. Knowledge of time-series modeling, predictive analytics, and statistical modeling. Experience with data visualization using Matplotlib, Seaborn, or dashboard integrations. Familiarity with big data frameworks (Spark, Hadoop) is a plus but not mandatory. Experience deploying ML models via APIs, microservices, or MLOps platforms. Understanding of cloud-based ML workflows (AWS Sagemaker, Azure ML, GCP Vertex AI or NIC cloud equivalents). Awareness of data privacy, model security, bias mitigation, and ethical AI practices. Ability to collaborate with domain teams, backend developers, and DevOps engineers. Strong documentation, experimentation, and analytical skills. Good to have: Google Professional Machine Learning Engineer, Microsoft Azure AI Engineer / Data Scientist, NVIDIA Deep Learning Certifications

📌 AI ML Lead (New Delhi)
🏢 EY
📍 New Delhi