Intuit's Horizon AI team is dedicated to building Autonomous Operations for Intuit's Expert Network. Our mission is to deliver AI-native solutions across forecasting, planning, optimization, and routing to drive clarity and efficiency in decision-making. In this role, you will be a technical lead embedded within a team of AI scientists and engineers. You will move beyond standard predictive modeling to tackle some of the hardest problems in enterprise operations: large-scale optimization and complex resource allocation. You will be responsible for designing and deploying systems that determine how thousands of experts are hired, scheduled, and assigned to millions of customers.
Requirements
- Strong proficiency in Python and software engineering fundamentals (data structures, algorithms, version control, CI/CD).
- Experience deploying highly scalable software supporting millions of users.
- Experience integrating applications with cloud technologies (i.e., AWS, GCP) and containerization (Docker/Kubernetes).
- Translate business constraints into mathematical formulations using Mixed-Integer Linear Programming (MILP) or Constraint Satisfaction.
- Build and scale machine learning models and optimization solvers using modern frameworks.
- Develop reliable pipelines and microservices that can handle millions of variables and decision points.
- Implement driver-based and attribute-driven forecasting models to predict demand (volume, handle-times) and supply availability, handling challenges like sparsity and hierarchical constraints.
Responsibilities
- Architect and implement enterprise-scale optimization engines for hiring/supply decisions and shift generation/activity assignment.
- Translate business constraints into mathematical formulations using Mixed-Integer Linear Programming (MILP) or Constraint Satisfaction.
- Build and scale machine learning models and optimization solvers using modern frameworks.
- Develop reliable pipelines and microservices that can handle millions of variables and decision points.
- Collaborate with scientists to implement driver-based and attribute-driven forecasting models to predict demand (volume, handle-times) and supply availability, handling challenges like sparsity and hierarchical constraints.
- Lead the transition from legacy heuristic models to robust, self-healing AI systems.
- Explore and implement emerging technologies, including Reinforcement Learning (RL) for real-time decision-making and agentic automation workflows.
Other
- 6+ years of industry experience in applied machine learning, AI engineering, or software engineering.
- Partner with product managers, operations leaders, and data scientists to define the roadmap for Autonomous Operations.
- Translate complex operational needs into technical AI solutions.
- Enforce high standards for code coverage, unit testing, and observability (OpEx) to ensure system stability.
- Cross-Functional Leadership
