AI / ML Engineer – Industrial Analytics

About Company

At Greenovative Energy, we’re not just building a company, we’re driving a movement toward net zero and sustainable progress. Our AI-powered platform uses fine-tuned models to understand real industrial behavior. It goes beyond monitoring to deliver clear, prescriptive actions from complex data. This helps manufacturing, energy, and utility teams reduce emissions while improving efficiency and cost performance.

Our Journey

It started with a simple problem.
Industrial energy data existed everywhere, but lived inside disconnected systems. Making sense of it required manual effort and deep expertise. We believed there had to be a smarter, scalable way.

So we began by building.
We created a universal platform that could connect to any machine in any factory, without replacing existing systems. By working directly in live, energy-intensive operations, we learned how energy behaves in the real world. As adoption grew, hundreds of millions of data points started flowing through the platform every day.

Scale changed everything.
As enterprises expanded across multiple plants, trust followed. The platform enabled comparison, benchmarking, and shared learning across operations. Greenovative crossed 150+ live deployments, serving the top 20% sector leaders, and expanded beyond India into the Middle East. Data volumes crossed 1 billion data points per day.

Today, intelligence drives action.
We’ve moved beyond visibility. Our AI prescribes actions, tracks real impact, and delivers consistent outcomes across enterprises. Operating as a pure software layer on top of existing systems, it helps organizations achieve 8–10% energy savings and make measurable progress toward sustainability and net-zero goals.

If you’re passionate about technology, data, and sustainability, and want to be part of a fast – growing, purpose-driven company, explore opportunities with us. Let’s build a smarter, more sustainable future—together.

Job description

This role focuses on applying Artificial Intelligence and Machine Learning techniques to solve real-world industrial and operational challenges. The position involves working with large-scale sensor, telemetry, and time-series data to develop intelligent analytics solutions that improve system efficiency, reliability, and performance.

Roles and Responsibilities

• Design, develop, and deploy machine learning models for industrial and operational analytics use cases, ensuring solutions are scalable, robust, and production-ready
• Develop predictive maintenance models to anticipate equipment failures and reduce unplanned downtime across industrial systems
• Build anomaly detection solutions to identify abnormal patterns in sensor and operational data, enabling early fault detection and proactive intervention
• Develop forecasting models for load, energy consumption, performance metrics, and operational trends using historical and real-time data
• Analyse large-scale sensor, telemetry, and time-series datasets, identifying patterns, correlations, and performance drivers
• Perform data pre-processing and feature engineering, including data cleaning, normalization, aggregation, and creation of domain-relevant features
• Design and maintain data pipelines for both real-time streaming data and batch data processing to support analytics and ML workflows
• Collaborate with cross-functional teams, including product, engineering, and domain experts, to translate business and operational problems into AI-driven solutions
• Optimize machine learning models for accuracy, computational efficiency, scalability, and deployment constraints
• Deploy models into production environments and integrate them with existing systems and applications
• Monitor model performance in production, track accuracy drift, data drift, and system behaviour, and implement continuous improvement strategies
• Document model logic, assumptions, workflows, and technical decisions to ensure maintainability and knowledge sharing

 Requirements:

• Strong foundation in Machine Learning and Statistics, including supervised and unsupervised learning techniques
• Proficiency in Python, with hands-on experience using libraries such as NumPy, Pandas, Scikit-learn, and PyTorch and/or TensorFlow
• Experience working with time-series data, including trend analysis, seasonality, and temporal modeling techniques
• Knowledge of predictive modeling, anomaly detection, and forecasting methods applicable to operational and industrial datasets
• Experience in data preprocessing and feature engineering, particularly for noisy and high-frequency data
• Familiarity with SQL and/or NoSQL databases for data extraction, storage, and analysis
• Understanding of data pipelines and ML workflows, including data ingestion, model training, validation, and deployment

• Good problem-solving, analytical, and debugging skills, with the ability to work on complex and ambiguous data challenges

Good to have:

• Experience working with industrial, manufacturing, energy, or IoT datasets
• Exposure to real-time data processing frameworks and streaming architectures
• Familiarity with cloud-based ML platforms and scalable deployment practices
• Understanding of model monitoring, versioning, and MLOps concepts