Senior AI/ML & Operations Research Engineer

seeking a Senior AI/ML & Operations Research Engineer to design, develop, and deploy enterprise-scale AI, machine learning, and mathematical optimization solutions focused on supply chain optimization, operational efficiency, and data-driven decision making.

The primary focus of this role is Operations Research and building optimization models . The ideal candidate will have hands-on experience developing and deploying mathematical models and advanced analytics solutions within operations and supply chain environments, combined with modern AI/ML engineering experience.

This individual will also support Generative AI initiatives, including Agentic AI applications, AI Agents, Retrieval-Augmented Generation (RAG), and LLM-powered solutions. The role requires someone who can take solutions from initial problem framing and model development through testing, deployment, monitoring, and continuous improvement.

Required Skills & Experience

• Strong hands-on Operations Research experience , with demonstrated experience building and deploying optimization and mathematical models. This is the most critical requirement.
• Experience developing optimization models such as Linear Programming (LP), Nonlinear Programming (NLP), Mixed Integer Programming (MIP), and Quadratic Programming (QP) and implementing decision-support solutions.
• 3 5+ years of AI/ML Engineering or advanced analytics experience , including deploying solutions into production environments.
• Strong experience with enterprise AI platforms and enterprise-scale AI/ML deployments , including supporting solutions through development, deployment, monitoring, and optimization.
• Strong Python development and data analysis skills.
• Experience building Generative AI and LLM applications .
• Hands-on experience with RAG architectures, Agentic AI, and AI Agents .
• Knowledge of prompt engineering, model optimization, and AI solution performance tuning .
• Experience with cloud-based AI/ML development and deployment.
• Experience supporting AI/ML infrastructure, MLOps, and CI/CD processes .
• Strong understanding of end-to-end software development and solution architecture.
• Experience working with databases, SQL, and large operational datasets.
• Ability to translate complex operational and supply chain problems into mathematical models and scalable technical solutions.
• Bachelor's degree in Operations Research, Industrial Engineering, Management Science, or a related quantitative discipline .

Nice to Have Skills & Experience

• Master's degree or Ph.D. in Operations Research, Industrial Engineering, Management Science, or a related quantitative discipline.
• Experience with optimization platforms/tools such as FICO, Gurobi, Lyric, CPLEX, GAMS, AMPL, AIMMS, Lingo, or Risk Solver Platform .
• Experience with simulation tools such as Arena, Crystal Ball, or Risk Solver Platform .
• Enterprise-scale Generative AI implementations.
• Strong MLOps and AI deployment experience.
• Previous experience developing analytics or optimization solutions for supply chain, manufacturing, logistics, procurement, materials management, or operations .

Responsibilities

• Design, develop, and deploy Operations Research optimization models that improve supply chain and operational decision making.
• Develop mathematical models and advanced analytics solutions that convert business problems into measurable, data-driven decision-support capabilities.
• Own or support the full optimization lifecycle including problem framing, data collection, model development, testing, deployment, results tracking, and continuous improvement .
• Design and develop end-to-end AI/ML pipelines and production-ready machine learning solutions.
• Build and deploy Agentic AI applications, AI Agents, RAG architectures, and LLM-powered solutions .
• Implement, optimize, monitor, and maintain AI/ML models within enterprise production environments.
• Support prompt engineering, model tuning, and AI solution performance optimization.
• Develop integrations with AI APIs, enterprise platforms, databases, and external services.
• Partner with business stakeholders across supply chain and operations to identify opportunities for optimization, advanced analytics, and AI.
• Contribute to MLOps processes, CI/CD pipelines, testing, validation, deployment, and AI infrastructure management.
• Develop solutions that can be adopted and sustained by business users for ongoing operational decision making.