About the Role
Are you the engineer who bridges the gap between data science and real-world industrial impact? We are looking for a Senior MLOps / Machine Learning Engineer to turn models into reliable production systems running against live plant and enterprise data.
You will own the entire path from notebook to production—deployment, monitoring, and retraining—including high-stakes models that feed back into plant control systems where stability and latency are non-negotiable. Without you, innovative pilots never industrialize.
Key Responsibilities
- Pipeline & Deployment: Build and own CI/CD pipelines for ML; package, deploy, and serve models in production (batch, real-time, and edge/on-prem plant environments).
- MLOps Infrastructure: Stand up and maintain infrastructure for experiment tracking, model registries, feature pipelines, and automated retraining.
- Monitoring & Reliability: Implement model monitoring (performance, data/concept drift, alerting) to ensure maximum uptime in plant settings.
- System Integration: Connect ML models with operational systems (DCS/control systems for advisory and closed-loop use, plant historians) and enterprise systems (SAP, Salesforce).
- Data Engineering: Build robust data pipelines feeding the lakehouse from both OT (Operational Technology) and IT sources.
- Collaboration: Partner with Data Scientists to productionize their work, and work alongside Platform/Security teams on OT/IT integration and cybersecurity.
- Best Practices: Champion software engineering excellence, including automated testing, versioning, reproducibility, and documentation.
Required Qualifications (Must-Have)
- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
- Experience: 3–6 years in ML Engineering, MLOps, Data Engineering, or Software Engineering with strong production ML exposure.
- Python Software Engineering: Advanced, production-grade Python (OOP, type hints, pytest, packaging, clean modular code, Git, and FastAPI/Flask).
- Classical ML Production: Strong command of scikit-learn pipelines, regularized regression, tree-based ensembles (XGBoost/LightGBM), clustering, and serialization (joblib/pickle/ONNX).
- DevOps & Orchestration: Hands-on experience with containerization (Docker), orchestration (Kubernetes), and workflow tools (Airflow / Prefect / Dagster).
- MLOps & Cloud Stack: Experience with MLOps tools (MLflow, Kubeflow, SageMaker, or Azure ML), Cloud platforms (Azure, AWS, or GCP), and Spark / Databricks.
- Data & CI/CD: Proficient in SQL, pandas, PySpark, Git-based CI/CD pipelines, and Infrastructure as Code (IaC).
Preferred Qualifications (Strong Pluses)
- Experience deploying ML models in industrial, manufacturing, or edge environments near plant equipment.
- Exposure to OT/IIoT integration (plant historians, OPC-UA, time-series databases, or Kafka streaming).
- Integration experience with SAP (ERP) and Salesforce (SFDC) data and APIs.
- Real-time/streaming ML and model optimization for ultra-low latency.
- Awareness of OT cybersecurity frameworks.
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Pay: Up to ₹1,200,000.00 per year
Application Question(s):
- Are you comfortable with a Vendor Contract role based fully on-site in Delhi?
- What is your notice period / availability to join?
- current salary per month?
- expected salary per month
- How many years of professional experience do you have in ML Engineering, MLOps, or Data Engineering with production ML exposure?
Experience:
- ML Engineer: 3 years (Required)
Work Location: In person
