AI/ML Engineer Job

Key Responsibilities:

a. Predictive Modeling & Deep Learning:
1. Develop ML/DL models for predicting match scores & outcomes, team & player performances, and statistics.
2. Implement time-series forecasting models (LSTMs, Transformers, ARIMA, etc.) for score predictions.
3. Train and fine-tune reinforcement learning models for strategic cricket decision-making.
4. Develop ensemble learning techniques to improve predictive accuracy.

b. In-Depth Cricket Analytics:
1. Design models to analyze player form, team strengths, matchups, and opposition weaknesses.
2. Build player impact and performance forecasting models based on pitch conditions, opposition, and recent form.
3. Extract insights from match footage and live tracking data using deep learning-based video analytics.

c. Data Processing & Engineering:
1. Collect, clean, and preprocess structured and unstructured cricket datasets from APIs, scorecards, and video feeds.
2. Build data pipelines (ETL) for real-time and historical data ingestion.
3. Work with large-scale datasets using big data tools (Spark, Hadoop, Dask, etc.).

d. Model Deployment & MLOps:
1. Deploy ML/DL models into production environments (AWS, GCP, Azure etc).
2. Develop APIs to serve predictive models for real-time applications.
3. Implement CI/CD pipelines, model monitoring, and retraining workflows for continuous improvement.

e. Performance Metrics & Model Explainability:
1. Define and optimize evaluation metrics (MAE, RMSE, ROC-AUC, etc.) for model performance tracking.
2. Implement explainable AI techniques to improve model transparency.
3. Continuously update models with new match data, player form & injuries, and team form changes.

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