Machine Learning Engineer

Job Title: Machine Learning Engineer   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.    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.  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.  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.  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.  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.  Proven track record of implementing ML or optimization models in production environments.  Strong mathematical and statistical foundation.  Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.    Shape a first-of-its-kind AI + clean energy platform .  A chance to leave your mark at the intersection of AI and sustainability .