Operations Machine Learning Engineer
New
J
JobgetherMachine learning
Ability to work effectively from a home office or another approved location within the United StatesFull-TimeMiddle
Salary$140,000–$163,000
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Job Details
- Experience
- At least three years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or a related technical field.
- Required Skills
- AWSPythonData engineeringSparkCI/CDDatabricksMLOps
Requirements
- Bachelor's degree in Computer Science, Data Engineering, Machine Learning Engineering, Engineering, Statistics, Mathematics, Data Science, or another quantitative or technical discipline.
- At least three years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or a related technical field.
- Experience building and maintaining production-grade data pipelines, machine learning workflows, or automation solutions using Python.
- Strong knowledge of cloud-based data technologies, preferably AWS and Databricks, Spark, or comparable platforms.
- Experience with CI/CD, deployment automation, version control, and fundamental software engineering practices.
- Understanding of data pipelines, orchestration, data quality management, feature engineering, and scalable data processing.
- Ability to collaborate with data science teams and translate analytical models and prototypes into reliable operational solutions.
- Ability to work effectively from a home office or another approved location within the United States and remain available during designated work hours.
- Experience deploying, monitoring, or maintaining predictive models, forecasting systems, recommendation engines, NLP applications, classification models, AI agents, or decision-support tools is preferred.
- Familiarity with infrastructure as code, cloud security, observability tools, model registries, workflow orchestration, or model monitoring platforms is preferred.
Responsibilities
- Transform machine learning models, analytical prototypes, and AI solutions into scalable, production-ready workflows.
- Design, develop, and maintain automated data and model pipelines using Python, AWS, Databricks, Spark, and related cloud technologies.
- Establish reusable deployment patterns, templates, CI/CD pipelines, documentation, and engineering standards.
- Collaborate with product, technology, data engineering, cloud engineering, DevOps, delivery platforms, and business operations teams.
- Develop monitoring frameworks for model performance, data quality, model drift, system reliability, usage, and business impact.
- Support model governance through documentation, auditability, explainability inputs, validation workflows, and model health assessments.
- Build feature engineering pipelines, curated data assets, model-serving workflows, and reusable operational data products.
- Investigate and resolve defects, performance issues, and process gaps in machine learning pipelines, data workflows, and deployment automation.
- Translate operational challenges and business objectives into technical requirements and communicate recommendations to technical and non-technical stakeholders.
- Contribute reusable tools, training materials, operational practices, and scalable MLOps standards.
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