Sr. AI & Machine Learning Engineer

Post Holdings Inc.•Brentwood, MO

7d•Hybrid

About The Position

Post Holdings is seeking a Sr. AI & Machine Learning Engineer to build and operationalize AI-powered business solutions. The role develops applications across predictive analytics, optimization, forecasting, recommendations, computer vision, and generative AI, including agentic workflows, copilots, and other intelligent applications. The engineer works with business stakeholders to identify requirements and outcomes and turns valuable opportunities into scalable production solutions. The position designs, builds, tests, and maintains production software incorporating AI and machine learning. This includes developing APIs, services, orchestration layers, enterprise integrations, and employee-facing applications, as well as reusable components and frameworks. The engineer develops model training, deployment, evaluation, monitoring, and retraining workflows; establishes engineering and CI/CD practices; and maintains system reliability, security, maintainability, and cost effectiveness. The role partners with product, data science, architecture, security, and platform teams; contributes to enterprise AI standards and governance; evaluates emerging technologies; and helps guide their adoption in line with architecture, roadmaps, and business priorities. It also helps establish scalable AI delivery practices and mentors engineers. This is a hybrid position based in St. Louis, Missouri, with in-office days on Tuesdays, Wednesdays, and Thursdays.

Requirements

Bachelor’s degree in Computer Science or a related technical field.
7+ years of software engineering experience.
Experience building production applications used by business users.
Strong proficiency in Python.
Experience designing APIs, services, integrations, and distributed systems.
Experience deploying machine learning models into production environments.
Experience with cloud platforms such as Azure.
Strong software engineering fundamentals, including testing, code quality, version control, and system design.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
5+ years of experience with data structures and algorithms.
3+ years of experience in a technical leadership role leading project teams and setting technical direction.
Experience building generative AI applications using Azure OpenAI, OpenAI, Anthropic, or similar platforms.
Experience with AI agents, orchestration frameworks, and Retrieval-Augmented Generation (RAG).
Experience with MLOps practices and machine learning lifecycle management.
Experience with React, TypeScript, or modern web application frameworks.
Experience with enterprise data platforms such as Snowflake, Databricks, or Fabric.
Experience working in manufacturing, supply chain, consumer goods, logistics, or enterprise operations.

Responsibilities

Develop predictive, optimization, forecasting, recommendation, computer vision, and generative AI applications.
Design and implement agentic workflows, copilots, and intelligent applications.
Work with business stakeholders to define requirements and measure outcomes, and turn high-value opportunities into scalable production solutions.
Design, build, test, and maintain software applications incorporating machine learning and AI capabilities.
Develop APIs, services, orchestration layers, and enterprise integrations.
Build user-facing applications that enable employees to interact with AI systems.
Create reusable components and frameworks to accelerate AI delivery across the enterprise.
Develop model training, deployment, evaluation, monitoring, and retraining workflows.
Establish engineering and CI/CD standards for AI application development.
Implement model observability, performance monitoring, and operational controls.
Ensure AI systems remain reliable, secure, maintainable, and cost effective.
Collaborate with Product Managers, Data Scientists, Architects, Security, and Platform teams.
Contribute to enterprise AI standards, approved development patterns, and governance processes.
Evaluate and prototype emerging AI technologies and translate promising advances into practical, scalable business solutions.
Recommend where new AI capabilities fit within the architecture, roadmap, and business priorities.
Help establish repeatable practices that enable AI delivery at scale.
Mentor engineers and elevate AI engineering capabilities across the organization.