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Data & AI Engineer
TrainTrackpanipat, haryana, in13 hours ago
Job descriptionDevelop and train models for : Scores and recommendations Predictive analytics Use both classical ML (regression, clustering) and neural approaches (LSTM, CNNs for time-series). Deploy models via FastAPI / Flask APIs for live inference in the app. Run experiments to improve model accuracy and personalization. Collaborate with physiotherapists and sports scientists to interpret results and validate model logic. Create dashboards to visualize performance metrics and prediction accuracy. Iterate based on pilot feedback from real users and physio sessions. Manage ML pipeline automation for retraining and continuous improvement. Ensure scalable, low-latency inference for user-facing features. Handle cloud storage and model versioning on AWS / GCP . Languages : Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow) Data : PostgreSQL / MongoDB / AWS S3 / BigQuery APIs : FastAPI, Flask Cloud : AWS / GCP (SageMaker, Lambda, EC2, Cloud Functions) Pipelines : Airflow / Prefect / custom ETL scripts Visualization : Matplotlib, Plotly, or Streamlit Familiarity with wearable and sensor data (accelerometer, HRV, GPS, power, etc.) Experience with time-series analysis, biomechanics, or exercise physiology Knowledge of recommendation systems or predictive analytics Experience with model deployment and MLOps Passion for fitness, health, or sports performance Strong communication — able to translate data science into plain English for non-technical teammates Comfortable with rapid prototyping and working in ambiguous early-stage environments Ownership of the entire AI backbone of a next-generation fitness platform. Freedom to experiment, deploy, and iterate models that directly impact users’ health outcomes. Work in close collaboration with experts in health tech. Competitive pay + early-stage equity options .
Key Responsibilities 1. Data Infrastructure & Pipelines
- Build and maintain data pipelines for collecting, cleaning, and storing data from multiple sources
- Design and manage databases for structured and time-series data
- Create tools for data validation, versioning, and ingestion to ensure model-ready datasets.
- Work closely with the Tech Lead to define API interfaces between the app and AI layer.
2. AI Model Development
3. Analysis & Experimentation
4. Deployment & Scaling
Ideal Background & Skills
Technical Stack :
Nice-to-Have :
Soft Skills :
What You’ll Get
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Data Engineer • panipat, haryana, in
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