Data & MLOps Engineer

The opportunity:

As an Data & MLOps Engineer, you will be part of Operation Center, India (INOPC-PG), aiming to develop a global value chain, where key business activities, resources, and expertise are shared across geographic boundaries to optimize value for Hitachi Energy customers across markets. As part of Transformers BU, we provide high-quality engineering and Technology to Hitachi Energy world. This is an important step from Hitachi Energy's Global Footprint strategy.

How you'll make an impact:

• Apply fundamental machine learning concepts to real-world business challenges.
• Assist in proposing project ideas and supporting analytics initiatives.
• Support the use of machine learning in engineering design and manufacturing workflows.
• Help build and test predictive models using techniques such as Neural Networks, Decision Trees, etc. Create Statistical Analysis, Hypothesis testing and experimentation on historical data across multiple sources to uncover hidden patterns.
• Work on Numerical Optimization techniques such as Evolutionary Algorithms, Gradient optimization, Reinforcement Learning etc.
• Work on consolidating and cleaning data from various SQL databases & cloud storage for analysis.
• Help with creating robust MLOps architecture and process using MS Azure & Databricks cloud platforms.
• Contribute to translating business requirements into technical solutions and model metrics.
• Present findings to team members and engage in continuous learning of new tools and libraries.
• Living Hitachi Energy's core values safety and integrity, which means taking responsibility for your own actions while caring for your colleagues and the business.

• Bachelor's (B.E/B.Tech) or Master's degree in Computer Science, Data Science, or a related field with 2-5 years of progressive experience.
• Basic experience with Python and relevant libraries (e.g., Scikit-learn, XGBoost, Tensorflow, Pytorch, Numpy, Pandas).
• Working knowledge of SQL or similar relational databases; comfortable with SQL scripting.
• Working knowledge of Statistical Analysis and Experimentation.
• Experience with using languages such as Python and C#.
• Experience with MS Azure and Databricks or similar cloud technologies.
• Interest or coursework in building machine learning models.
• Willingness to learn new tools and libraries within the MLOps lifecycle.