Operations Machine Learning Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Operations Machine Learning Engineer based in the United States. The Operations Machine Learning Engineer will play a key role in transforming machine learning models and AI solutions into reliable, scalable tools that improve operational efficiency and business outcomes. This position focuses on MLOps, cloud platform engineering, and data engineering, enabling data science innovations to move seamlessly from experimentation into production. You will build automated pipelines, deployment frameworks, and monitoring systems that support workforce productivity, client satisfaction, and data-driven decision-making. Working closely with data scientists, software engineers, cloud specialists, and business stakeholders, you will establish repeatable engineering practices that ensure reliability, scalability, and responsible AI adoption. You will also contribute to model governance, data quality, and performance monitoring across a diverse range of operational use cases. This fully remote opportunity is ideal for an engineer who enjoys solving complex technical challenges and building the infrastructure that turns AI innovation into measurable business value. Accountabilities: Machine Learning Operationalization: Partner with Operations Data Science teams to transform machine learning models, analytical prototypes, and AI solutions into scalable, production-ready workflows. Pipeline Development and Automation: Design, develop, and maintain automated data and model pipelines using Python, AWS, Databricks, Spark, and related cloud technologies. MLOps Engineering: Establish reusable deployment patterns, templates, CI/CD pipelines, documentation, and engineering standards that streamline the transition from experimentation to production. Cloud Platform Integration: Collaborate with Product and Technology, Data Engineering, Cloud Engineering, DevOps, Delivery Platforms, and business operations teams to ensure solutions align with enterprise architecture, security requirements, and platform standards. Model Monitoring and Reliability: Develop monitoring frameworks to track model performance, data quality, model drift, system reliability, usage, and measurable business impact. Responsible AI and Governance: Support model governance through documentation, auditability, explainability inputs, validation workflows, and ongoing model health assessments. Data Engineering and Feature Development: Build feature engineering pipelines, curated data assets, model-serving workflows, and reusable operational data products that support multiple business applications. Troubleshooting and Continuous Improvement: Identify, investigate, and resolve defects, performance issues, and process gaps in machine learning pipelines, data workflows, and deployment automation. Business Requirements Translation: Convert operational challenges and business objectives into technical requirements, communicating recommendations and complex concepts clearly to both technical and non-technical stakeholders. Cross-Functional Project Delivery: Participate in collaborative working sessions, provide technical guidance, share recommendations, and help coordinate project execution across data science, technology, and business teams. Engineering Standards and Best Practices: Contribute to the development of reusable tools, training materials, operational practices, and scalable MLOps standards that improve consistency and efficiency across the organization. Requirements: Education: Bachelor's degree in Computer Science, Data Engineering, Machine Learning Engineering, Engineering, Statistics, Mathematics, Data Science, or another quantitative or technical discipline. Professional 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. Python Development: Demonstrated experience building and maintaining production-grade data pipelines, machine learning workflows, or automation solutions using Python. Cloud and Data Platforms: Strong knowledge of cloud-based data technologies, preferably AWS and Databricks, Spark, or comparable platforms. CI/CD and Software Engineering: Experience with continuous integration and continuous delivery, deployment automation, version control, and fundamental software engineering practices. Data Engineering Fundamentals: Understanding of data pipelines, orchestration, data quality management, feature engineering, and scalable data processing. Machine Learning Deployment: Ability to collaborate with data science teams and translate analytical models and prototypes into reliable, maintainable operational solutions. Monitoring and Observability: Familiarity with monitoring model performance, data drift, system reliability, and production workflows is beneficial. Responsible AI Practices: Knowledge of model governance, explainability, auditability, validation, risk management, and documentation practices for machine learning systems is an advantage. Collaboration and Communication: Strong interpersonal and communication skills, including the ability to explain technical concepts to business stakeholders and non-technical audiences. Independent Problem-Solving: Ability to work autonomously, deliver high-quality results, seek feedback when appropriate, and contribute to shared technical standards and best practices. Preferred Machine Learning Experience: Experience deploying, monitoring, or maintaining predictive models, forecasting systems, recommendation engines, natural language processing applications, classification models, AI agents, or decision-support tools. Preferred Business Applications: Experience supporting AI or machine learning solutions in operations, customer service, workforce management, CRM, business intelligence, or other business process environments. Preferred Cloud and MLOps Tools: Familiarity with infrastructure as code, cloud security, observability tools, model registries, workflow orchestration, and model monitoring platforms. Preferred Industry Knowledge: Familiarity with HR, payroll, human capital management (HCM), service operations, client retention, or voice-of-client analytics is an advantage. Physical and Technical Requirements: Ability to work at a desk for extended periods, typically seven to eight hours daily, and operate computers, phone systems, and multiple software applications simultaneously. Remote Work Requirements: Ability to work effectively from a home office or another approved location within the United States and remain available during designated work hours. Benefits: Annual Base Salary: $140,000–$163,000, depending on job-related knowledge, technical skills, professional experience, and market location. Performance-Based Compensation: Eligibility for quarterly bonuses and restricted stock unit grants based on individual performance. Employment Type: Full-time position. Remote Work: Fully remote opportunity within the United States, with no regular in-office requirement under the current arrangement. Work arrangements may change based on business needs and individual performance. Healthcare Coverage: Medical, dental, and vision insurance. Financial Protection: Life insurance and disability coverage. Retirement Benefits: 401(k) retirement plan with employer matching contributions. Family and Personal Support: Additional benefits and perks designed to support employees, their families, and financial well-being. Career Development: Opportunities to develop technical expertise, expand machine learning and cloud engineering skills, and contribute to innovative AI initiatives. Collaborative Environment: Work alongside data scientists, engineers, technology specialists, and business teams on impactful operational challenges. Innovation and Impact: Help implement practical AI solutions that improve business processes, workforce productivity, client outcomes, and operational decision-making. #J-18808-Ljbffr Royper Royco LLC