ML Engineer Senior

Encardio Rite Group; India; Hybrid; INR 1,800,000 - 2,400,000; Full time

Job summary

Encardio-Rite Electronics Pvt. Ltd. is seeking an ML Engineer to design, develop and deploy ML models that analyze real-time sensor data from geotechnical instruments, enabling anomaly detection and predictive maintenance for infrastructure projects worldwide.

The role offers end-to-end MLOps exposure within a robust cloud/Kubernetes environment, using modern tools and data streaming from Kafka. Collaboration with firmware, backend and domain teams is essential.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electronics, Civil/Geotechnical Engineering, or a related field.
• Experience with ML model development and deployment in production.
• Strong knowledge of Python and ML libraries.

Responsibilities

• Design and develop ML models for time-series analysis and forecasting.
• Build predictive maintenance solutions from sensor data.
• Manage end-to-end ML pipelines with MLflow.
• Deploy models as microservices on Kubernetes (AWS EKS).
• Ingest data streams from Kafka and collaborate with IoT teams.
• Monitor model performance with Prometheus and Grafana; enable retraining.

Skills

Python

ML pipelines

Time-series analysis

CI/CD

Kubernetes

Kafka

Observability

Git

Education

Bachelor's or Master's degree

Tools

MLflow

Argo Workflows

Airflow

Kubeflow

Docker

PostgreSQL

TimescaleDB

Job description

Job Description:

Department

Edge Technology Center

Location

Lucknow / Delhi (Hybrid / Remote)

Job Type

Full-Time, Permanent

Experience

5–8 Years

Position

ML Engineer

About The Role

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.

This role offers end-to-end exposure—from data exploration to deployment—within a robust MLOps ecosystem using modern tools and cloud infrastructure.

Key Responsibilities

• 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.

Qualifications

• 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.