Lead AI/ML Engineer (P4645)

LEAD AI/ML ENGINEER (P4645)

SUMMARY

As a Lead AI/ML Engineer (G3) on the Labs team, you will serve as a hands-on technical lead at the intersection of classical optimization science and modern AI/ML. Our Labs team has a strategic focus on building AI-enhanced optimization systems, designing AI layers that augment, accelerate, and extend classical optimization engines to unlock solutions that neither approach achieves alone. This is not a generalist ML role: you will bring deep optimization foundations and use them as the platform on which the next generation of intelligent, adaptive systems are built.
You will contribute code daily, serve as one of the team's primary subject-matter experts on optimization formulations, solver selection, and hybrid architecture design, mentor engineers and researchers, and partner with cross-functional stakeholders to define, deliver, and scale production systems across Kroger.

RESPONSIBILITIES
-Serve as a hands-on developer responsible for building and maintaining end-to-end ML, AI, and optimization-based solutions
-Design and build hybrid AI/optimization systems including ML-guided search, learned warm starts, neural network surrogate models, and AI-augmented constraint formulations that improve the performance, scalability,interpretability and adaptability of classical optimization solvers
-Lead technical design, implementation, and review processes for POCs and production-ready systems
-Lead end-to-end solution lifecycle—from rapid prototyping through to scaling and hand-off to production teams in partnership with other data scientists and engineers within Labs and across the business
-Serve as one of the team's primary technical resources on optimization problem formulations, solver selection, and performance benchmarking across constraint types and problem scales
-Partner with researchers and data scientists to co-develop, scale, and operationalize new algorithms
-Architect and implement robust ML(AI)Ops pipelines that support experimentation, deployment, and monitoring
-Build reusable ML components and APIs that enable modularity and scalability across business areas
-Evaluate and adopt emerging technologies and tooling that can enhance experimentation and delivery speed
-Drive technical best practices in code quality, documentation, observability, and team knowledge sharing
-Drive experimentation and benchmarking to select performant solutions that balance complexity and business value
-Contribute to Labs’ collaborative, research-forward culture by learning, sharing, and mentoring both junior and senior engineers and researchers on industry-leading and cutting-edge technologies
-Lead and participate in code reviews and technical architecture planning to ensure adherence to preferred patterns and standards
-Represent Labs in technical forums; proactively mentor junior and peer engineers
-Collaborate with product and business stakeholders to align technical execution with innovation goals

REQUIRED QUALIFICATIONS
-Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field
-4+ years experience experience developing ML, AI, or optimization systems, including production deployment and scaling
-Strong software engineering fundamentals and daily coding experience in Python
-Deep proficiency in Python and fluency in NumPy, pandas, PySpark and at least 3 of the following MLand Optimization libraries - PyTorch, TensorFlow, scikit-learn, and Pyomo (Pyomo proficiency is specifically required).
-Hands-on experience architecting and productionizing at least one type of optimization problem (e.g., network optimization, vehicle routing, scheduling, facility location, or resource allocation).
-Practical experience with at least two industry-standard optimization solvers such as Gurobi, CPLEX, OR-Tools, Pyomo, PuLP, CBC, or SCIP.
-Demonstrated experience designing or prototyping hybrid AI/optimization systems where ML or AI components (surrogate models, learned heuristics, prediction models, AI chatbots) interact with or augment classical optimization solvers
-Hands-on experience designing CI/CD and MLOps workflows using tools such as MLflow, Azure ML, or Databricks
-Familiarity with cloud platforms (Azure preferred), containerization (Docker), and orchestration (Kubernetes)
-Experience with modern software development practices including testing, logging, observability, and version control
-Ability to lead projects through ambiguity and collaborate in highly cross-functional teams

PREFERRED EXPERIENCE
-Deep knowledge of operations research fundamentals such as linear programming, integer programming, mixed-integer programming, constraint programming, stochastic optimization, or combinatorial optimization
-Experience integrating reinforcement learning, neural combinatorial optimization, or other ML-driven approaches with classical solver frameworks (e.g., ML-guided branching, policy-based heuristics, or graph neural networks for combinatorial problems)
-Familiarity with applied research at the optimization/AI/ML intersection such as learning to optimize, predict-then-optimize, end-to-end differentiable optimization, algorithm selection via ML, AI assisted optimization.
-Strong track record of partnering with researchers to translate early-stage ML ideas into deployable systems
-Experience prototyping and scaling AI solutions in applied environments
-Experience designing experiment platforms or reusable ML/optimization infrastructure
-Demonstrated leadership in evaluating trade-offs between performance, complexity, and maintainability
-Familiarity with real-time or batch data processing systems
-Leadership in navigating trade-offs between performance, complexity, and long-term maintainability

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