Staff Machine Learning Engineer, Platform – MLOPS

Credit Acceptance

. Own end-to-end deployment paths for ML and GenAI models, including training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA, and production .

Posted 10/23/2026full-timeUnited StatesLead💰 $184,943 - $258,920 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing end-to-end deployment paths for ML and GenAI models, including production monitoring, model serving infrastructure, and incident response. Proficient in Python and SQL, with hands-on experience in cloud ML platforms and production-quality engineering practices.

Highest-signal resume keywords

Production ML Or AI SystemsPython ProgrammingSQL ProficiencyCloud ML Platform ExperienceModel Serving Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Training PipelinesInference PipelinesModel RegistryVersion ControlCI/CDContainerizationInfrastructure As CodeModel ServingMonitoring And AlertingData Governance

Soft Skills

Problem SolvingCommunicationMentoringRelationship BuildingTask Prioritization

Tools & Technologies

AWSDatabricksMLflowOpenTelemetryDynatraceAgentCore GatewayModel Context Protocol (MCP)

Certifications & Qualifications

Bachelor's Degree In Computer ScienceMaster's Degree In Relevant Field

Industry Keywords

AI SystemsRegulated IndustriesFinancial ServicesProduction MonitoringObservability

Tech Stack

Tools & technologies

AWSCloudPythonSQLUnity

About the role

Key responsibilities & impact

Own end-to-end deployment paths for ML and GenAI models, including training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA, and production
Own runtime health for production models through monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling, and incident response through root cause and corrective action
Operate the agent runtime layer through the enterprise AI Gateway and MCP Gateway
Migrate existing agents onto governed gateway paths and maintain scoped, versioned, least-privilege tool surfaces
Own production evaluation of agents, including online scoring, behavioral monitoring, drift detection, sampling and judge pipelines, and release gates
Build and maintain observability and evaluation infrastructure, including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and data contracts
Measure and manage platform unit economics, including cost per inference, document, and interaction
Produce platform and infrastructure cost analysis for build-versus-buy and hosting decisions
Deliver reusable pipeline templates, deployment patterns, reference implementations, and internal tooling
Partner with Cloud Engineering, Data Engineering, Security, and SRE on enterprise governance, identity, and observability
Respond to AI-specific production incidents and drive closed corrective actions
Maintain architecture documentation and system diagrams for the ML platform
Mentor engineers and interns on production ML practices
Follow company policies, processes, legal guidelines, and department attendance requirements

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Statistics, or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience
5+ years building and operating production ML or AI systems
Direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting
Demonstrated ownership of a production ML or AI service through its full operating life
Strong Python and SQL
Production-quality engineering practice, including version control, testing, code review, and CI/CD applied to ML workloads
Hands-on experience with a cloud ML platform in production; AWS and Databricks strongly preferred
Experience with model serving, job orchestration, and a model registry or experiment tracking system such as MLflow
Working knowledge of LLM and GenAI operational characteristics, including token cost, latency, caching, batching, and non-deterministic output
Experience running LLM or agent applications behind a gateway or proxy layer
Knowledge of model routing, fallback, credential and key management, rate limiting, and budget enforcement
Working knowledge of tool-calling architecture, including the Model Context Protocol (MCP)
Experience with containerization and infrastructure as code
Ability to communicate technical and non-technical trade-offs clearly in writing
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California
Required degrees must be from institutions accredited by the Council for Higher Education Accreditation or equivalent
Preferred: production experience with GPU-backed model serving
Preferred: experience with OpenTelemetry and an enterprise observability platform such as Dynatrace
Preferred: model serving efficiency techniques including quantization, parameter-efficient fine-tuning, distillation, or inference optimization
Preferred: data governance and access control on a lakehouse platform such as Databricks Unity Catalog
Preferred: AI systems experience in regulated industries, particularly financial services
Preferred: agentic or multi-step AI systems in production
Preferred: managed agent or tool gateway experience such as AWS Bedrock AgentCore Gateway
Preferred: familiarity with Agent2Agent (A2A) style delegation, agent discovery, capability advertisement, and workload identity
Ability to communicate complex technical information to all levels, including senior leadership
Ability to solve problems at the source with simple, working solutions
Ability to respond promptly and effectively to incidents, tasks, and projects
Demonstrated ability and motivation to teach others
Ability to build relationships across the organization
Ability to prioritize and execute tasks in a high-pressure environment

Benefits

Comp & perks

Annual variable incentive of cash and equity, between 10–20% of base salary
401(K) match
Adoption assistance
Parental leave
Tuition reimbursement
Comprehensive medical, dental, and vision coverage
Nonstandard benefits
Hybrid work model with two work-from-home days per week