AI/ML Platform Engineer
About The Position
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.
Requirements
- 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.
- 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
- Effective Communication
- Results Orientation
- Learning Agility
- Digital Fluency
- Customer Centricity
Nice To Haves
- AWS Certified Machine Learning Specialty
Responsibilities
- Develop and manage internal platform tools and services, including self-service portals/workbenches, backend APIs (Python/FastAPI), automations, and CI/CD tooling.
- Integrate MCP/gateway integrations and AI-enabled automations and flows, focusing on reducing friction for teams adopting the platform.
- Write and maintain Terraform for provisioning and configuring platform resources across AWS and Azure.
- Diagnose deployment, networking, endpoint, and configuration issues.
- Participate in standups, syncs, and planning/project meetings.
- Conduct design and architecture reviews, and perform regular PR and code reviews.
- Onboard new teams, translate ambiguous requirements into practical plans, and challenge weak designs.
- Provide model deployment support, assisting teams with productionizing models (hosting options, inference patterns, scaling, cost, and operational readiness across SageMaker, Bedrock, Azure ML/AI Foundry, and Kubernetes).
- Create and maintain documentation, onboarding guides, and reference examples.
- Proactively identify and fix process and platform improvements.
Benefits
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
