Machine Learning Engineer

Job Title :

Machine Learning Engineer

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 and visionaries with decades of expertise in finance, policy, technology, and innovation, we are committed to making long-term efforts to fulfil this vision through our technical innovation, client services, expertise, and capability expansion.

Role Summary :

We are seeking a highly skilled and analytical

Machine Learning Engineer

to design, develop, and optimize advanced ML and optimization models that power intelligent, data-driven decision systems. The ideal candidate will focus on building predictive, prescriptive, and optimization-based solutions to solve complex real-world challenges. This role involves close collaboration with data scientists, engineers, and domain experts to build scalable, production-ready ML systems.

Key Responsibilities :

Design, develop, and implement

optimization and forecasting models

for complex analytical problems.

Apply

mathematical programming

(linear, nonlinear, integer, and mixed-integer optimization) to operational and decision-making scenarios.

Build, train, and fine-tune

machine learning and statistical models

for regression, classification, and clustering.

Conduct

feature selection, hyperparameter tuning, and performance evaluation

using systematic validation techniques.

Integrate ML and optimization algorithms within business or system logic for intelligent decision-making.

Collaborate with cross-functional teams to ensure models are scalable, interpretable, and production-ready.

Stay updated with advancements in

optimization algorithms, ML model architectures, and applied AI techniques

Education & Experience :

Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or related quantitative fields.

1–2 years of hands-on experience in

machine learning

optimization

, or

forecasting

Strong background in

mathematical modeling, statistics, or operations research

Experience in

production-grade model deployment

and performance monitoring.

Skills Required :

The candidate should have strong proficiency in Python, with practical experience for building optimization models.

A solid understanding of linear programming, mixed-integer programming, convex optimization, stochastic optimization, and constraint programming is essential.

The role requires experience in algorithm design, numerical methods, and sensitivity analysis for robust model development.

The candidate should possess a strong background in operations research and optimization model formulation.

Experience in model validation and performance tuning is required to ensure accuracy and efficiency.

The role involves performing scenario analysis and supporting optimization-based decision-making.

Knowledge of workflow automation and integration of optimization models with APIs will be an advantage.

Proficiency in version control using Git.

The candidate should demonstrate strong problem-solving and analytical thinking abilities.

Clear documentation skills and the ability to communicate technical results effectively are essential.

What You’ll Do :

Build and train ML and optimization models for production-scale use cases.

Design intelligent data workflows and pipelines to support predictive and prescriptive analytics.

Collaborate with engineering and research teams to develop scalable ML solutions.

Develop APIs and tools to make ML capabilities accessible across systems.

Continuously monitor, maintain, and enhance model accuracy and scalability.

What you will 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