Senior Machine Learning Platform Engineer — Model Hosting & MLOps

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.

You will work with model developers application engineers security and cloud platform teams to turn a model artifact into a secure scalable inference service. The work spans serving architecture infrastructure as code deployment automation model lifecycle management and production operations across AWS and Azure with potential integration into on-premises environments. You will also help shape agent workflows that use hosted models so serving choices support the needs of multi-step applications. Success means teams can deploy update monitor and troubleshoot models through clear reusable platform patterns.

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 $147050 to $230850 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 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 HPsEEO Policy or read about your rights as an applicant under the law here: Know Your Rights: Workplace Discrimination is Illegal