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About This Role
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 $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 "
Salary Context
This $147K-$230K range is above the median for MLOps Engineer roles in our dataset (median: $172K across 12 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,079 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At HP, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $204,000 based on 162 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($188K) sits 7% below the category median. Disclosed range: $147K to $230K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
HP AI Hiring
HP has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Vancouver, WA, US, Spring, TX, US, Spring, TX. Compensation range: $161K - $230K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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