Machine Learning Engineer

Job Title:

Machine Learning Engineer 

Location: Bengaluru, Karnataka

About the Company:

Sustainability 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

.