Senior Machine Learning Platform Engineer – Model Hosting, MLOps

HP France

. Design and build hosting for custom ML and AI models across AWS and Azure, focusing on GPU-backed LLM inference and real-time endpoints .

Posted 10/6/2026full-timeSpring • Texas • United StatesSenior💰 $147,050 - $230,850 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building hosting solutions for ML and AI models across AWS and Azure, with a strong focus on GPU-backed LLM inference and MLOps workflows. Proficient in infrastructure as code, Kubernetes management, and secure deployment patterns.

Highest-signal resume keywords

GPU Inference OptimizationKubernetes Provisioning and ConfigurationInfrastructure as Code (Terraform)ML Deployment PipelinesAWS and Azure ML Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingMLOps WorkflowsModel Serving PlatformsBatch Inference SupportAutomated PipelinesModel Registration and VersioningProduction MonitoringIncident Diagnosis and ImprovementSecure Deployment PatternsAgent Workflow Development

Soft Skills

Collaboration Across TeamsDocumentation Skills

Tools & Technologies

AWS SageMakerAzure Machine LearningKubernetesEKSAKSTerraform

Industry Keywords

LLM InferenceContainerized WorkloadsHybrid Model HostingIAM/RBACSecrets Management

Tech Stack

Tools & technologies

AWSAzureCloudKubernetesPythonTerraform

About the role

Key responsibilities & impact

Design and build hosting for custom ML and AI models across AWS and Azure, focusing on GPU-backed LLM inference and real-time endpoints
Support batch inference where appropriate
Package models and dependencies into reproducible serving workloads
Select and implement managed services, containers, or Kubernetes-based hosting patterns
Provision and configure Kubernetes clusters or other hosting infrastructure, including compute, storage, API access, identity and access controls, secrets, networking, observability, and environment configuration
Design secure connections and deployment patterns between cloud and on-premises environments
Develop infrastructure as code and deployment automation
Build MLOps workflows for model registration, versioning, validation, release, rollback, and retirement
Connect training or model preparation to deployment through automated pipelines and quality gates
Partner with application teams to design and prototype agent workflows
Translate agent workload patterns into hosting decisions regarding model selection, context length, concurrency, latency, cost, tool access, and tracing
Establish production monitoring for service health, latency, throughput, errors, GPU and resource use, and model behavior
Diagnose incidents and improve capacity, reliability, and cost
Create reusable deployment templates, reference architectures, documentation, and onboarding paths
Partner with model and application teams on inference, serving, scaling, evaluation, data handling, and operational ownership tradeoffs

Requirements

What you’ll need

Hands-on experience hosting LLM inference on GPU infrastructure in a production environment
Experience building model-serving platforms for custom models, including 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 ML infrastructure across AWS and Azure, with deep hands-on delivery in at least one and practical ability to work in the other
Experience with 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
Working knowledge of production concerns for inference services, including scaling, latency, availability, logging and metrics, incident response, access control, and cost
Ability to work across model development, application, platform, and security teams and document operational approaches
Advanced GPU inference optimization, including capacity planning, batching, autoscaling, memory use, model loading, and performance tuning
Experience serving generative or other compute-intensive custom models beyond LLMs
Familiarity with AWS SageMaker or EKS, Azure Machine Learning or AKS, or equivalent platforms
Experience with hybrid or on-premises model hosting
Familiarity with model registries, experiment tracking, data or feature pipelines, scheduled retraining, model evaluation, and drift or quality monitoring
Experience with secure enterprise deployment patterns such as private networking, IAM/RBAC, secrets management, auditability, and sensitive data handling
Experience building shared ML platform capabilities or self-service workflows
Hands-on development of agent workflows, including tool integration, evaluation, tracing, or guardrails

Benefits

Comp & perks

Bonus and/or equity opportunities (United States of America candidates only)
Health insurance
Dental insurance
Vision insurance
Long term/short term disability insurance
Employee assistance program
Flexible spending account
Life insurance
4-12 weeks fully paid parental leave based on tenure
11 paid holidays
Additional flexible paid vacation and sick leave