Senior Machine Learning Platform Engineer — Model Hosting & MLOps

HP; Spring (TX); On-site; USD 147,000 - 231,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program

Flexible spending account

Life insurance

Generous time off

Job summary

HP is seeking a Senior Machine Learning Platform Engineer to build infrastructure and workflows that move custom models from development to production. You will host LLMs on GPU infrastructure, shape cloud-hosted model serving, and create repeatable MLOps pipelines for release, monitoring, and cost control.

You will design hosting across AWS and Azure, implement IaC, and collaborate with application teams on agent workflows, data handling, and security.

Qualifications

• Hands-on experience hosting LLM inference on GPU infrastructure in production.
• Experience building model-serving platforms with inference runtimes, endpoints, scaling, and tooling.
• Strong Python software engineering skills for services and automation.
• Experience delivering ML workloads on AWS or Azure with IaC (Terraform or equivalent).
• Experience provisioning and maintaining Kubernetes for containerized inference workloads.
• Experience building ML deployment pipelines with versioning, validation, CI/CD, and rollback.

Responsibilities

• Design hosting for ML/AI models across AWS and Azure with GPU-backed LLM inference and real-time endpoints.
• Package models into reproducible serving workloads using containers or Kubernetes patterns.
• Provision and configure Kubernetes clusters, including IAM, networking, observability, and security.
• Develop IaC and deployment automation for repeatable provisioning and promotion of environments.
• Build MLOps workflows for model registration, versioning, validation, release, and rollback.
• Collaborate with teams on agent workflows, including tool integration and tracing.

Skills

LLM inference hosting

Python software engineering

Infrastructure as code

Kubernetes production hosting

AWS/Azure deployment

Tools

Terraform

Kubernetes

AWS

Azure

Job description

Role summary

We are hiring a Senior Machine Learning Platform Engineer to build the infrastructure and engineering workflows that take custom models from development into reliable production use. This is a hands-on role for someone who has hosted LLMs on GPU infrastructure, developed the cloud platform around model serving, and built MLOps pipelines that make releases repeatable and observable.

What You Will Do

• Design and build hosting for custom ML and AI models across AWS and Azure, with particular focus on GPU-backed LLM inference and real-time endpoints; support batch inference where appropriate.
• Package models and their dependencies into reproducible serving workloads; choose and implement suitable managed services, containers, or Kubernetes-based patterns based on throughput, latency, security, reliability, and cost.
• Provision and configure Kubernetes clusters or other suitable hosting infrastructure, including compute, storage, API access, identity and access controls, secrets, networking integration, observability, and environment configuration.
• Help design secure connections and deployment patterns between cloud and on-premises environments as hosting needs evolve.
• Develop infrastructure as code and deployment automation so model-hosting environments can be provisioned, reviewed, promoted, and maintained consistently.
• Build MLOps workflows for model registration, versioning, validation, release, rollback, and retirement. Connect training or model preparation to deployment through automated pipelines and appropriate quality gates.
• Partner with application teams to design and prototype agent workflows, including model and tool orchestration, state handling, failure recovery, and evaluation.
• Translate agent workload patterns into hosting decisions about model selection, context length, concurrency, latency, cost, tool access, and end-to-end tracing.
• Establish production monitoring for service health, latency, throughput, errors, GPU and other resource use, and model behavior. Share responsibility for diagnosing incidents and improving capacity, reliability, and cost.
• Create reusable deployment templates, reference architectures, documentation, and onboarding paths that help other teams ship models safely.
• Partner with model and application teams on practical tradeoffs such as online versus batch inference, managed versus self-hosted serving, scaling, evaluation, data handling, and operational ownership.

Required Experience

• Hands-on experience hosting LLM inference on GPU infrastructure in a production environment. You can explain what you personally built, how models reached production, and how you managed throughput, latency, utilization, reliability, and cost.
• Experience building the surrounding model-serving platform for custom models, such as inference runtimes, deployment patterns, endpoint access, scaling, and operational tooling.
• Strong software engineering skills, especially Python, with experience building services, automation, and maintainable production code.
• Experience developing infrastructure for ML workloads across AWS and Azure, with deep hands-on delivery in at least one and practical ability to work in the other. You have used infrastructure as code such as Terraform or an equivalent tool.
• Experience provisioning, configuring, and maintaining Kubernetes or another production hosting platform for containerized inference workloads.
• Experience building or operating ML deployment pipelines with versioned artifacts, automated validation, CI/CD, environment promotion, and rollback.
• Familiarity with LLM agent patterns, including model invocation, tool calls, and multi-step workflows, and how they affect serving capacity, reliability, and access controls.
• Working knowledge of production concerns for inference services: scaling, latency, availability, logging and metrics, shared incident response, access control, and cost.
• Ability to work across model development, application, platform, and security teams; turn ambiguous requirements into a working design; and document the resulting operational approach.

Helpful Experience

• Advanced GPU inference optimization, including capacity planning, batching, autoscaling, memory use, model loading, and performance tuning.
• Serving generative or other compute-intensive custom models beyond LLMs; selecting and tuning inference servers and runtime configurations.
• AWS SageMaker or EKS; Azure Machine Learning or AKS; or equivalent managed and self-hosted model platforms.
• Hybrid or on-premises model hosting, including connectivity, security boundaries, hardware constraints, and operational handoff.
• Model registries, experiment tracking, data or feature pipelines, scheduled retraining, model evaluation, and drift or quality monitoring.
• Secure enterprise deployment patterns such as private networking, IAM/RBAC, secrets management, auditability, and handling sensitive data.
• Building shared ML platform capabilities or self-service workflows used by multiple engineering teams.
• Hands-on development of agent workflows, including tool integration, evaluation, tracing, or guardrails.

Who Will Thrive Here

You are an engineer who has taken responsibility for what happens after a model is trained: how it is packaged, deployed, secured, scaled, observed, updated, and supported. You are comfortable writing code and infrastructure, investigating production failures, and making clear tradeoffs with partner teams.

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

knowledge, skills, and experience.

Benefits

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.

Job

Software

Schedule

Full time

Shift

No shift premium (United States of America)

Travel

No

Relocation

No

Equal Opportunity Employer (EEO)

HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s). Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence. For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: "Know Your Rights: Workplace Discrimination is Illegal"