Lead AI/ML Engineer

Lead AI/ML/Optimization Engineer (G3) – Labs Innovation Focus (P3227)

SUMMARY
As a Lead AI/ML Engineer (G3) on the Labs team, you will serve as a hands-on technical lead responsible for both implementing robust code and guiding the architectural direction of ML/AI/optimization-based systems. This role blends deep engineering expertise, applied ML and optimization research, and system design to accelerate the transition from proof-of-concept to scalable business solution. You will contribute code daily, mentor junior engineers, and collaborate with cross-functional partners to define, deliver, and scale the next generation of AI/ML/optimization capabilities across Kroger.

RESPONSIBILITIES

• Serve as a hands-on developer responsible for building and maintaining end-to-end ML, AI, and optimization-based solutions
• 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
• 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.
• 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 one industry-standard optimization solver such as Gurobi, CPLEX, OR-Tools, Pyomo, PuLP, CBC, or SCIP.
• 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

• 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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