Optimization and ML Modelling Engineer

Description

• Apply mathematical programming (linear, nonlinear, integer, and mixed-integer optimization) to operational and decision-making scenarios.
• Translate business objectives into mathematical formulations that can be optimized effectively.
• Shape a first-of-its-kind AI + clean energy platform.
• Design, develop, and implement optimization and forecasting models for complex analytical problems.
• Collaborate with engineering and research teams to develop scalable ML solutions.
• Design intelligent data workflows and pipelines to support predictive and prescriptive analytics.
• 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.
• Continuously monitor, maintain, and enhance model accuracy and scalability.
• Integrate ML and optimization algorithms within business or system logic for intelligent decision-making.
• 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.
• Build and train ML and optimization models for production-scale use cases.
• Develop APIs and tools to make ML capabilities accessible across systems.
• The role involves performing scenario analysis and supporting optimization-based decision-making.

Requirements

We are seeking a highly skilled Optimization and ML Modelling Engineer to design, develop, and deploy prescriptive models that identify optimal solutions under complex business and operational constraints. The role focuses on formulating and solving optimization problems using advanced mathematical programming and numerical methods. The ideal candidate will combine strong analytical thinking with hands-on Python development to build models that drive intelligent, data-informed decision-making.

• A solid understanding of linear programming, mixed-integer programming, convex optimization, stochastic optimization, and constraint programming is essential.
• The candidate should possess a strong background in operations research and optimization model formulation.
• Strong proficiency in Python and libraries such as SciPy,Pyomo, NumPy, OR-Tools, PuLP, Gurobi, or CPLEX.
• Experience in model validation and performance tuning is required to ensure accuracy and efficiency.
• Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.
• The role requires experience in algorithm design, numerical methods, and sensitivity analysis for robust model development.
• Proficiency in version control using Git.
• 1–2 years of hands-on experience in machine learning, optimization.
• Strong mathematical and statistical foundation.
• Passion for scalable systems, automation, and applied problem-solving.
• Experience in production-grade model deployment and performance monitoring.
• Proven track record of implementing ML and optimization models in production environments.
• Strong background in mathematical modeling, statistics, or operations research.
• Clear documentation skills and the ability to communicate technical results effectively are essential.
• The candidate should demonstrate strong problem-solving and analytical thinking abilities.
• Knowledge of workflow automation and integration of optimization models with APIs will be an advantage.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or related quantitative fields.
• The candidate should have strong proficiency in Python, with practical experience for building optimization models.

Benefits

• A chance to leave your mark at the intersection of AI, Optimization and sustainability.
• Work with a small, mission-driven team obsessed with impact.

Training + Development

Interview process

Visa Sponsorship

Security clearance