Roles and Responsibilities
The successful candidate will work in AI/ML team, to design and develop advanced anamoly detection models for industrial assets like Pumps, Moters, Blowers etc. As a part of Industry 4.0 solutions for smart manufacturing, smart water, smart building and smart manufacturing, t he candidate will support in developing value add analytic using the senor data.
The candidate will be contributing in design and development of Cimcon Digital IoT platform. Important pillars of Cimcon Digital IoT Platform are Secure communication, Machine Learning, Cloud connectivity and Edge computing, the candidate will work with multiple teams to maintain balance on all the four areas.
- Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.
- Managing available resources such as hardware, data, CPU cores and memory so that multiple models can be run on single cloud machine.
- Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.
- Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
- Verifying data quality, and/or ensuring it via data cleaning.
- Supervising the data acquisition process if more data is needed.
- Finding available data-sets online that could be used for training.
- Defining validation strategies.
- Defining the pre-processing or feature engineering to be done on a given data-sets.
- Defining data augmentation pipelines.
- Training models and tuning their hyperparameters.
- Analyzing the errors of the model and designing strategies to overcome them.
- Deploying models to production.
- Architecture of end to end pipeline.
- Must have worked on big data architecture such as Hadoop or Spark.
- Should be expert in python.
- Preferred to have worked in sensor telemetry data and IoT devices to detect anomaly.
- Strong knowledge for ML/DL models for anomaly detection in timeseries data.
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