AI/ML Platform Engineer

HP•Spring, TX

3d•$147,050 - $230,850•Onsite

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.
Proficiency in Python (Programming Language).
Experience with Amazon Web Services (AWS).
Experience with Microsoft Azure.
Knowledge of Agile Methodology.
Understanding of Algorithms.
Experience with Artificial Intelligence.
Experience with Automation.
Knowledge of Big Data.
Experience with Deep Learning.
Knowledge of Machine Learning.
Experience with Natural Language Processing.
Familiarity with PyTorch (Machine Learning Library).
Familiarity with Scikit-learn (Machine Learning Library).
Strong Software Engineering skills.
Experience with TensorFlow.
Effective Communication skills.
Results Orientation.
Learning Agility.
Digital Fluency.
Customer Centricity.

Nice To Haves

AWS Certified Machine Learning Specialty certification.
Experience with C++ (Programming Language).
Knowledge of Computer Science.
Experience with Data Science.
Experience with Java (Programming Language).
Experience with Apache Spark.

Responsibilities

Develop and manage internal platform tools and services, including self-service portals/workbenches, backend APIs (Python/FastAPI), automations, and CI/CD tooling.
Integrate with MCP/gateway and develop AI-enabled automations and flows.
Write and maintain Terraform for provisioning and configuring platform resources across AWS and Azure.
Diagnose and resolve 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 requirements into practical plans, and challenge weak designs.
Support teams in productionizing models, including hosting options, inference patterns, scaling, cost, and operational readiness across various cloud platforms (SageMaker, Bedrock, Azure ML/AI Foundry, Kubernetes).
Create and maintain documentation, onboarding guides, and reference examples.
Proactively identify and fix process and platform improvements.