AI/ML Platform Engineer

We are a dynamic centralized platform team dedicated to harnessing cutting-edge AI/ML technology, particularly in the realm of Generative AI and large language models, to empower HP and drive innovation. Collaborating closely with various business units, we provide strategic advice, prototype solutions, and develop and manage software applications tailored for internal use.

Days split roughly evenly between hands-on building and collaboration/enablement, driven by a mix of roadmap work and incoming requests. Expect to shift context often.

Building (largest share of the day)

• Internal platform tools and services: self-service portals/workbenches, backend APIs (Python/FastAPI), automations and CI/CD tooling
• MCP/gateway integrations and AI-enabled automations and flows
• Focus is always on reducing friction for teams adopting the platform

Cloud infrastructure & troubleshooting (weekly)

• Writing and maintaining Terraform; provisioning and configuring platform resources across AWS and Azure
• Diagnosing deployment, networking, endpoint, and configuration issues
• Enough depth to reason about deployments and partner with security/networking specialists

Collaboration & Enablement (about Half The Day)

• Standups, syncs, and planning/project meetings
• Design and architecture reviews; regular PR and code review
• Onboarding new teams; translating ambiguous requirements into practical plans and challenging weak designs

Model deployment support (recurring)

• Helping teams productionize models—hosting options, inference patterns, scaling, cost, and operational readiness across SageMaker, Bedrock, Azure ML/AI Foundry, and Kubernetes

Docs & platform improvement (ongoing)

• Documentation, onboarding guides, and reference examples
• Ad-hoc process and platform improvements—spotting and fixing rough edges proactively

In short: a builder-first role with a strong collaborative and enablement component—someone who moves fluidly between writing code, reviewing work, troubleshooting infrastructure, and guiding architectural decisions.

Education & Experience Recommended

• Four-year or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, or any other related discipline or commensurate work experience or demonstrated competence.
• Typically has 7-10 years of work experience, preferably in computer programming languages, machine learning, algorithms, statistical methods, or a related field.

Preferred Certifications

AWS Certified Machine Learning Specialty

Knowledge & Skills

• Agile Methodology
• Algorithms
• Amazon Web Services
• Apache Spark
• Artificial Intelligence
• Automation
• Big Data
• C++ (Programming Language)
• Computer Science
• Data Science
• Deep Learning
• Java (Programming Language)
• Machine Learning
• Microsoft Azure
• Natural Language Processing
• Python (Programming Language)
• PyTorch (Machine Learning Library)
• Scikit-learn (Machine Learning Library)
• Software Engineering
• TensorFlow

Cross-Org Skills

• Effective Communication
• Results Orientation
• Learning Agility
• Digital Fluency
• Customer Centricity

Pay & Benefits

The pay range for this role is $147,050 to $230,850 USD annually with additional

opportunities for pay in the form of bonus and/or equity (applies to United

States of America candidates only). Pay varies by work location, job-related

Benefits

knowledge, skills, and experience.

HP offers a comprehensive benefits package for this position, including:

• Health insurance
• Dental insurance
• Vision insurance
• Long term/short term disability insurance
• Employee assistance program
• Flexible spending account
• Life insurance
• Generous time off policies, including;
• 4-12 weeks fully paid parental leave based on tenure
• 11 paid holidays
• Additional flexible paid vacation and sick leave
• US benefits overview https://hpbenefits.ce.alight.com/

The compensation and benefits information is accurate as of the date of this

posting. The Company reserves the right to modify this information at any time,

with or without notice, subject to applicable law.