ML Engineer (Senior)

Encardio Rite Group; Delhi; Híbrido; INR 3.000.000 - 4.500.000; Jornada completa

Descripción de la vacante

Encardio-Rite Electronics Pvt. Ltd. is seeking a passionate ML Engineer to join the AI & Data Science team at the Edge Technology Center.

You will design, develop, and deploy ML models to analyze real-time sensor data from geotechnical instruments for anomaly detection and predictive maintenance. The role offers end-to-end exposure from data exploration to deployment within a robust MLOps ecosystem using modern tools and cloud infrastructure, collaborating with firmware, backend, and domain

Formación

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electronics, Civil/Geotechnical Engineering, or related field.
• 5–8 years of experience in ML/DS with production deployments.
• Experience designing time-series ML models and deploying ML pipelines.

Responsabilidades

• Design and develop ML models for time-series analysis, anomaly detection, and forecasting.
• Build predictive maintenance solutions by identifying early warning signals in sensor data.
• Develop end-to-end ML pipelines using MLflow and workflow tools.
• Deploy models as scalable microservices on Kubernetes (AWS EKS).
• Ingest data from real-time streams via Kafka and collaborate with IoT teams.
• Monitor model performance with Prometheus and Grafana; implement retraining.
• Conduct EDA and experimentation using JupyterHub.
• Collaborate with firmware, backend, and domain experts.
• Maintain documentation and participate in CI/CD processes.

Conocimientos

Python

Time-series

MLOps

Cross-functional

Educación

Bachelor’s or Master’s in CS/DS/Engineering

Herramientas

MLflow

Kubernetes

Docker

Kafka

Prometheus

Descripción del empleo

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.