Sustainability Economics
Website:
sustainabilityeconomics.com
Job details:
Location: Bengaluru, Karnataka
About the Company
Sustainability Economics.ai is a global organization pioneering the convergence of clean energy and AI, enabling profitable energy transitions while powering end-to-end AI infrastructure. By integrating AI-driven cloud solutions with sustainable energy, we create scalable, intelligent ecosystems that drive efficiency, innovation, and long-term impact across industries. Guided by exceptional leaders with deep expertise across finance, policy, and technology, we are committed to building impactful, future-ready solutions.
Role Summary
We are seeking a highly skilled and analytical Machine Learning Engineer to design, develop, and deploy advanced machine learning and forecasting models for real-world applications.
The ideal candidate will have strong expertise in machine learning algorithms, time series analysis, and probabilistic forecasting, along with working knowledge of optimization techniques to enhance decision-making systems.
This role focuses on building scalable, production-grade ML systems and collaborating closely with data scientists, engineers, and domain experts to deliver impactful solutions.
Key Responsibilities
- Design, develop, and deploy machine learning models for regression, classification, clustering, and anomaly detection.
- Build and optimize time series forecasting models for real-world datasets.
- Develop probabilistic forecasting models to quantify uncertainty and support risk-aware decision-making .
- Apply advanced ML techniques including ensemble methods, deep learning models, and sequence models (LSTM, RNNs).
- Perform feature engineering, feature selection, and preprocessing for structured and time series data.
- Conduct model validation, backtesting, and hyperparameter tuning using robust statistical methods.
- Build scalable pipelines for training, inference, and monitoring of ML models in production environments.
- Integrate ML models into APIs and business systems for real-time and batch predictions.
- Apply optimization techniques (linear / mixed-integer / heuristic methods) to enhance model-driven decisions.
- Collaborate with cross-functional teams to ensure models are interpretable, scalable, and aligned with business needs.
- Stay updated with advancements in machine learning, time series forecasting, and applied AI.
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, Statistics, or related quantitative fields.
- 1–3 years of hands-on experience in machine learning and forecasting.
- Strong understanding of statistics, probability, and time series analysis.
- Experience deploying ML models into production environments.
Skills Required
- Strong proficiency in Python (NumPy, Pandas, Scikit-learn, etc.).
- Experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
- Solid understanding of machine learning algorithms:
- Regression (Linear, Ridge, Lasso)
- Classification (Logistic Regression, Random Forest, Gradient Boosting)
- Clustering (K-Means, Hierarchical)
- Hands-on experience with time series analysis and forecasting techniques:
- ARIMA / SARIMA
- Exponential smoothing
- Prophet
- LSTM / RNN-based models
- Experience with probabilistic forecasting methods:
- Quantile regression
- Prediction intervals / confidence intervals
- Bayesian approaches
- Probabilistic time series models (e.g., DeepAR, Gaussian Processes)
- Evaluation metrics such as pinball loss, CRPS
- Strong knowledge of model evaluation, validation strategies, and backtesting.
- Experience with data pipelines and workflow automation.
- Familiarity with basic optimization techniques (linear programming, heuristics) is a plus.
- Experience in building and consuming REST APIs.
- Proficiency with Git/version control.
- Strong analytical thinking and problem-solving skills.
- Ability to clearly communicate technical concepts and results.
What You’ll Do
- Build and deploy end-to-end ML and forecasting systems.
- Develop intelligent data pipelines for predictive and probabilistic analytics.
- Work on real-world time series problems in energy, infrastructure, and sustainability domains.
- Collaborate with engineering teams to integrate ML models into scalable systems.
- Monitor and improve model performance, accuracy, and reliability.
- Contribute to building AI-driven decision-making platforms.
What You’ll Bring
- Proven track record of implementing ML or optimization models in production environments.
- Strong mathematical and statistical foundation.
- Passion for scalable systems, automation, and applied problem-solving.
- Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.
Why Join Us
- Shape a first-of-its-kind AI + clean energy platform.
- Work with a small, mission-driven team obsessed with impact.
- An aggressive growth path.
- A chance to leave your mark at the intersection of AI and sustainability.
Click on Apply to know more.