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AI/ML Ops Specialist

5 - 9 years

0 Lacs

haryana

Posted:20 hours ago| Platform: Shine logo

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Skills Required

engineers predictive maintenance process optimization anomaly detection engineering model validation performance monitoring plant operations predictive modeling aiml specialist ot sensor data azure ml tools data scientists operations teams timeseries sensor data manufacturing problems ai use cases mlops pipelines databricks delta lake azure iot edge retraining pytorch tensorflow timeseries modeling edge inferencing manufacturing kpis

Work Mode

On-site

Job Type

Full Time

Job Description

As an AI/ML Specialist, you will be responsible for building intelligent systems utilizing OT sensor data and Azure ML tools. Your primary focus will be collaborating with data scientists, engineers, and operations teams to develop scalable AI solutions addressing critical manufacturing issues such as predictive maintenance, process optimization, and anomaly detection. This role involves bridging the edge and cloud environments by deploying AI solutions to run effectively on either cloud platforms or industrial edge devices. Your key functions will include designing and developing ML models using time-series sensor data from OT systems, working closely with engineering and data science teams to translate manufacturing challenges into AI use cases, implementing MLOps pipelines on Azure ML, and integrating with Databricks/Delta Lake. Additionally, you will be responsible for deploying and monitoring models at the edge using Azure IoT Edge, conducting model validation, retraining, and performance monitoring, as well as collaborating with plant operations to contextualize insights and integrate them into workflows. To qualify for this role, you should have a minimum of 5 years of experience in machine learning and AI. Hands-on experience with Azure ML, ML flow, Databricks, and PyTorch/TensorFlow is essential. You should also possess a proven ability to work with OT sensor data such as temperature, vibration, flow, etc. A strong background in time-series modeling, edge inferencing, and MLOps is required, along with familiarity with manufacturing KPIs and predictive modeling use cases.,

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