ML Engineer (Senior)

We are seeking a passionate and skilled ML Engineer to join our AI & Data Science team at Encardio-Rite Electronics Pvt. Ltd. The role involves designing, developing, and deploying machine learning models to analyze real-time sensor data from geotechnical instruments. The selected candidate will contribute to building intelligent monitoring systems enabling anomaly detection, predictive maintenance, and structural health forecasting for critical infrastructure globally., * Design and develop machine learning models for time-series analysis, anomaly detection, and forecasting.

• Build predictive maintenance solutions by identifying early warning signals in sensor data.
• Develop and manage end-to-end ML pipelines using tools like MLflow and workflow orchestration platforms.
• Deploy models as scalable microservices on Kubernetes (AWS EKS) using containerization tools.
• Integrate real-time data streams from Kafka and collaborate with IoT teams for data ingestion.
• Monitor model performance using observability tools like Prometheus and Grafana, and implement retraining mechanisms.
• Conduct exploratory data analysis and experimentation using JupyterHub.
• Collaborate with cross-functional teams including firmware, backend, and domain experts.
• Maintain proper documentation and participate in code reviews and CI/CD processes.

Key Deliverables

• Accurate and scalable ML models for anomaly detection and forecasting.
• Robust and automated ML pipelines with proper experiment tracking.
• Deployment-ready ML services integrated with real-time data streams.
• Continuous monitoring and improvement of model performance.
• Documentation and adherence to engineering and MLOps best practices.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electronics, Civil/Geotechnical Engineering, or a related field.

Technical Skills

• Strong proficiency in Python and ML libraries (scikit-learn, PyTorch/TensorFlow, pandas, NumPy).
• Experience in time-series analysis (feature engineering, forecasting, anomaly detection).
• Hands-on experience in ML model development and deployment in production environments.
• Knowledge of MLOps tools such as MLflow and workflow orchestration tools (Argo Workflows/Airflow/Kubeflow).
• Experience with Kubernetes (EKS), Docker, and container-based deployments.
• Understanding of data streaming platforms like Kafka.
• Proficiency in SQL and databases like PostgreSQL or TimescaleDB.
• Familiarity with CI/CD tools (GitHub Actions) and version control (Git).
• Exposure to monitoring tools like Prometheus and Grafana.

Soft Skills

• Strong analytical and problem-solving abilities.
• Effective communication and collaboration skills.
• Ability to work in a cross-functional and agile environment.
• Self-driven with a learning mindset and attention to detail.
• Capability to manage multiple tasks and meet deadlines.