About the role

You are the engineer who turns models into reliable production systems running against live plant and enterprise data. You own the path from notebook to production deployment, monitoring, retraining including the demanding case of models that feed back into plant control systems where stability and latency are non-negotiable. Without this role, pilots never industrialize.

Key responsibilities

- Build and own CI/CD pipelines for ML; package, deploy and serve models in production (batch, real-time, and edge / on-prem as plant environments demand).
- Stand up and maintain MLOps infrastructure experiment tracking, model registry, feature pipelines and automated retraining.
- Implement model monitoring (performance, data and concept drift, alerting) and ensure reliability and uptime in plant settings.
- Integrate models with operational systems DCS / control systems for advisory and closed-loop use, plant historians and with enterprise systems (SAP, Salesforce).
- Build robust data pipelines feeding the lakehouse from both OT and IT sources.
- Partner with data scientists to productionise their work, and with platform / security teams on OT/IT integration and cybersecurity.
- Champion engineering best practices: testing, versioning, reproducibility and documentation.

Required qualifications (must-have)

- Bachelor's or Master's in Computer Science, Engineering, or a related field.
- 36 years in ML engineering, MLOps, data engineering or software engineering with production ML exposure.
- Strong Python software-engineering fundamentals OOP, type hints, automated testing (pytest), packaging, clean modular code, and version control.
- Solid working command of classical ML (scikit-learn pipelines, regularised regression, tree-based ensembles, clustering) enough to package, serve,



optimise and monitor these models reliably in production.
- Hands-on with containerisation (Docker), orchestration (Kubernetes) and workflow tools (Airflow / Prefect / Dagster).
- Experience with MLOps tooling (MLflow, Kubeflow, SageMaker, Azure ML or similar) and model serving.
- Cloud experience (Azure, AWS or GCP) and a modern data stack (Spark / Databricks).
- CI/CD (Git-based pipelines) and familiarity with infrastructure-as-code.

Preferred (strong pluses)

- Experience deploying ML in industrial / manufacturing settings, including edge or on-prem deployment near plant equipment.
- Exposure to OT / IIoT integration plant historians, OPC-UA, time-series databases, streaming (Kafka).
- Integration experience with SAP (ERP) and Salesforce (SFDC) data and APIs.
- Real-time / streaming ML and model optimisation for low latency.
- Awareness of OT cybersecurity considerations.

Technical skills

- Python software engineering: advanced, production-grade Python OOP, type hints, automated testing (pytest), packaging & dependency management, profiling and performance; building model APIs with FastAPI / Flask; strong SQL and Git.
- Classical ML in production: working command of scikit-learn pipelines and classical models (regularised regression, tree-based ensembles XGBoost / LightGBM, clustering); model serialisation (joblib / pickle / ONNX); batch and real-time inference.
- Data engineering: pandas and PySpark for batch processing; streaming with Kafka; data validation (pydantic, Outstanding Expectations); feature pipelines / feature stores.
- MLOps & infrastructure: Docker, Kubernetes, MLflow, Airflow / Prefect, CI/CD and infrastructure-as-code; model registries and serving frameworks.

Platform & tooling

Cloud (Azure / AWS / GCP); Databricks / Spark; time-series databases and historian / OPC-UA connectors.

📌 Senior ML Engineer (Gurugram)
🏢 JK Cement
📍 Gurugram