Senior Machine Learning Platform Engineer, Model Hosting & MLOps

HP Inc.

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Spring, TXTlaquepaque, Jal., Mexico
Salary
$147,050–$230,850 / yr
Posted
6 days ago
Freshness
Confirmed live yesterday
Closes
Apr 4, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k
This role $189k
$131k $293k
below market most similar roles pay here above market

This role pays less than 77% of similar roles. Most pay $196,750–$254,750 — the blue band above. At the midpoint, this role pays about $189k versus about $226k for comparable roles.

Based on 240 similar postings.

Employer

About HP Inc.

HP Inc. is a global technology company that develops and sells personal computers, printers, and related supplies and services. Its products include laptops, desktops, workstations, and printing solutions for consumers and businesses worldwide.

HP Inc. currently has 116 open roles on FindRole.

Listed pay typically runs $130,700–$205,200 across 96 roles with salary data.

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View all roles at HP Inc.

At a glance

TL;DR · Senior Machine Learning Platform Engineer, Model Hosting & MLOps

The Senior Machine Learning Platform Engineer — Model Hosting & MLOps joins the team to build infrastructure and engineering workflows that transition custom models into reliable production use. This role involves designing and building hosting for custom ML and AI models across AWS and Azure, with a specific focus on GPU-backed LLM inference and real-time endpoints. You will package models into reproducible serving workloads using containers or Kubernetes-based patterns, develop infrastructure as code using Terraform, and build MLOps pipelines for model registration, versioning, and automated validation. Key responsibilities include establishing production monitoring for latency and throughput, designing secure cloud-to-on-premises connections, and prototyping agent workflows. You will utilize Python to build maintainable production code while managing tradeoffs between online and batch inference, scaling, and cost.

What you'll do

  • Design and build hosting for custom ML models on GPU-backed infrastructure across AWS and Azure.
  • Package models and dependencies into reproducible serving workloads using containers or Kubernetes.
  • Provision and configure Kubernetes clusters including compute, storage, networking, and identity controls.
  • Develop infrastructure as code and deployment automation to maintain consistent hosting environments.
  • Build MLOps workflows for model registration, versioning, validation, automated release, and rollback.
  • Design secure deployment patterns and connections between cloud and on-premises environments.
  • Establish production monitoring for service health, latency, throughput, and GPU resource utilization.
  • Create reusable deployment templates and reference architectures to help other teams ship models.

What we're looking for

  • Hands-on experience hosting LLM inference on GPU infrastructure in a production environment.
  • Experience building model-serving platforms including inference runtimes, deployment patterns, and operational tooling.
  • Strong software engineering skills in Python for building services, automation, and maintainable production code.
  • Experience developing ML infrastructure across AWS and Azure using infrastructure as code tools like Terraform.
  • Experience provisioning and maintaining Kubernetes or other production hosting platforms for containerized inference workloads.
  • Experience building ML deployment pipelines with versioned artifacts, automated validation, CI/CD, and rollback capabilities.
  • Familiarity with LLM agent patterns, including model invocation, tool calls, and multi-step workflows.
  • Working knowledge of production concerns including scaling, latency, availability, logging, metrics, and cost.

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