Operations Machine Learning Engineer

Paylocity

. Convert machine learning models, analytical prototypes, and AI solutions into scalable production-ready workflows .

Posted 10/13/2026full-timeRemote • United StatesMid-LevelSenior💰 $140,000 - $163,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable production-ready machine learning workflows and automated data pipelines using Python and cloud technologies like AWS and Databricks. Proficient in CI/CD practices and capable of translating business needs into technical requirements while ensuring model governance and performance monitoring.

Highest-signal resume keywords

Machine Learning EngineeringPython DevelopmentAWS Cloud TechnologiesCI/CD PracticesData Engineering Concepts

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringPython DevelopmentData Pipeline DevelopmentFeature EngineeringDeployment AutomationModel GovernanceMonitoring FrameworksData Quality AssuranceVersion ControlScalable Processing

Soft Skills

Strong Communication SkillsCollaborationProblem SolvingIndependenceStakeholder Engagement

Tools & Technologies

AWSDatabricksSparkCI/CD ToolsData Platforms

Industry Keywords

MLOpsData EngineeringAnalytical PrototypesOperational WorkflowsTechnical Requirements

Tech Stack

Tools & technologies

AWSCloudPythonSpark

About the role

Key responsibilities & impact

Convert machine learning models, analytical prototypes, and AI solutions into scalable production-ready workflows
Design, build, and maintain automated data and model pipelines using Python, AWS, Databricks or similar cloud data platforms, Spark, and CI/CD practices
Develop reusable deployment patterns, templates, documentation, and engineering standards
Collaborate with Product & Technology, Data Engineering, Cloud Engineering, DevOps, Delivery Platforms, and business operations teams
Create monitoring frameworks for model performance, data quality, drift, reliability, usage, and business impact
Support responsible AI and model governance practices, including documentation, auditability, explainability inputs, validation workflows, and model health checks
Develop feature engineering pipelines, curated data assets, model-serving workflows, and operational data products
Identify and resolve issues, defects, or process gaps in machine learning pipelines, data workflows, and deployment automation
Translate operational business needs into technical requirements
Communicate technical concepts to non-technical stakeholders
Participate in cross-functional working sessions and provide technical recommendations
Help drive project execution across Data Science, business, and technology teams

Requirements

What you’ll need

Bachelor's degree in computer science, data engineering, machine learning engineering, engineering, statistics, mathematics, data science, or another quantitative or technical field
At least 3 years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or similar technical roles
Experience building production-grade data or machine learning workflows in Python
Strong background with cloud data technologies, preferably AWS and Databricks, Spark, or similar platforms
Experience with CI/CD practices, deployment automation, version control, and software engineering fundamentals
Knowledge of data engineering concepts, including data pipelines, data quality, orchestration, feature engineering, and scalable processing
Demonstrated ability to partner with data science teams and translate analytical solutions into reliable operational workflows
Ability to work independently, deliver high-quality work, seek input from others, and contribute to shared standards and best practices
Strong communication skills, including the ability to explain technical concepts to business stakeholders and non-technical audiences
Availability five days per week during designated work hours
Ability to sit at a desk or workstation for extended periods, typically 7–8 hours a day
Ability to operate a computer, use phone systems, type, and use multiple software programs simultaneously

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
401(k) match
Perks supporting employees, their families, and finances
Career development opportunities
Quarterly bonus
Restricted stock unit grant based on individual performance
Fully remote work from home or location of record within the U.S.
No in-office requirements