Senior Ai/Ml

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.