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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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.