AI Product Engineer

McKesson•Irving, TX

9d•$108,700 - $181,100•Hybrid

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. Mckesson's Group Purchasing Organization (GPO) team delivers innovative sourcing, contracting, analytics, and operational solutions that help healthcare organizations improve efficiency, reduce costs, and make informed business decisions. By combining industry expertise with data-driven insights and technology-enabled solutions, the GPO organization supports critical business functions across McKesson and its customers. As a Senior AI Product Engineer, you will design, build, and own AI-powered products that support complex business workflows and decision-making processes. This role combines product ownership, software engineering, and AI solution development to deliver scalable tools that create measurable business value. You will work closely with business stakeholders, data teams, and technology partners to transform business challenges into AI-driven products that improve efficiency, automation, and insight generation. This position is remote and candidates must reside within commuting distance of either The Woodlands, TX or Irving/Las Colinas, TX and be available to attend onsite meetings as needed.

Requirements

Typically requires 4+ years of related experience and a bachelor's degree in computer science, engineering, information systems, data science, or a related field, or equivalent experience.
4+ years of software engineering experience with proficiency in Python and SQL.
Experience designing and implementing data pipelines, integrations, and cloud-based applications.
Experience building AI, machine learning, generative AI, intelligent workflows or LLM-powered solutions.
Knowledge of modern cloud platforms such as Azure, AWS, or Google Cloud Platform.
Experience integrating third-party data sources and APIs.
Full-stack development experience, including front-end application development and data visualization.
Ability to translate business requirements into technical solutions and product capabilities.
Strong analytical, problem-solving, and critical-thinking skills.
Strong verbal and written communication skills with the ability to collaborate across technical and business teams.

Nice To Haves

Experience delivering solutions independently in dynamic and evolving environments.
Experience developing products from concept through deployment and user adoption.
Familiarity with AI governance, model evaluation, prompt engineering, and solution monitoring.
Ability to influence stakeholders and drive alignment without formal authority.
Demonstrated curiosity, innovation, and continuous learning in emerging AI technologies.

Responsibilities

Partner with business subject matter experts to understand workflows and translate requirements into scalable product solutions.
Collaborate with cross-functional teams to identify opportunities for automation and process optimization.
Design, develop, and deploy AI-powered applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), intelligent workflows, and related technologies.
Build and maintain data pipelines and integrations required to support AI-powered solutions.
Integrate internal and external data sources to create scalable, reliable, and actionable intelligence solutions.
Implement controls that improve solution quality, including source attribution, confidence scoring, auditability, and human review processes where appropriate.
Develop user-focused products and experiences that drive adoption and measurable business impact.
Define, monitor, and report key success metrics including user adoption, productivity gains, operational efficiencies, and business outcomes.
Support production AI applications through monitoring, troubleshooting, and continuous improvement efforts.
Document technical designs, data flows, governance considerations, and operational procedures.
Partner with data engineering, analytics, and operations teams to leverage enterprise data assets and shared technology platforms.
Contribute to the broader AI strategy by evaluating emerging technologies and recommending innovative solutions.