Operations Machine Learning Engineer

<h3>Accountabilities:</h3><li>
<strong>Machine Learning Operationalization:</strong>Partner with Operations Data Science teams to transform machine learning models, analytical prototypes, and AI solutions into scalable, production-ready workflows.</li><li>
<strong>Pipeline Development and Automation:</strong>Design, develop, and maintain automated data and model pipelines using Python, AWS, Databricks, Spark, and related cloud technologies.</li><li>
<strong>MLOps Engineering:</strong>Establish reusable deployment patterns, templates, CI/CD pipelines, documentation, and engineering standards that streamline the transition from experimentation to production.</li><li>
<strong>Cloud Platform Integration:</strong>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.</li><li>
<strong>Model Monitoring and Reliability:</strong>Develop monitoring frameworks to track model performance, data quality, model drift, system reliability, usage, and measurable business impact.</li><li>
<strong>Responsible AI and Governance:</strong>Support model governance through documentation, auditability, explainability inputs, validation workflows, and ongoing model health assessments.</li><li>
<strong>Data Engineering and Feature Development:</strong>Build feature engineering pipelines, curated data assets, model-serving workflows, and reusable operational data products that support multiple business applications.</li><li>
<strong>Troubleshooting and Continuous Improvement:</strong>Identify, investigate, and resolve defects, performance issues, and process gaps in machine learning pipelines, data workflows, and deployment automation.</li><li>
<strong>Business Requirements Translation:</strong>Convert operational challenges and business objectives into technical requirements, communicating recommendations and complex concepts clearly to both technical and non-technical stakeholders.</li><li>
<strong>Cross-Functional Project Delivery:</strong>Participate in collaborative working sessions, provide technical guidance, share recommendations, and help coordinate project execution across data science, technology, and business teams.</li><li>
<strong>Engineering Standards and Best Practices:</strong>Contribute to the development of reusable tools, training materials, operational practices, and scalable MLOps standards that improve consistency and efficiency across the organization.</li><h2>Requirements:</h2><li>
<strong>Education:</strong>Bachelor's degree in Computer Science, Data Engineering, Machine Learning Engineering, Engineering, Statistics, Mathematics, Data Science, or another quantitative or technical discipline.</li><li>
<strong>Professional Experience:</strong>At least three years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or a related technical field.</li><li>
<strong>Python Development:</strong>Demonstrated experience building and maintaining production-grade data pipelines, machine learning workflows, or automation solutions using Python.</li><li>
<strong>Cloud and Data Platforms:</strong>Strong knowledge of cloud-based data technologies, preferably AWS and Databricks, Spark, or comparable platforms.</li><li>
<strong>CI/CD and Software Engineering:</strong>Experience with continuous integration and continuous delivery, deployment automation, version control, and fundamental software engineering practices.</li><li>
<strong>Data Engineering Fundamentals:</strong>Understanding of data pipelines, orchestration, data quality management, feature engineering, and scalable data processing.</li><li>
<strong>Machine Learning Deployment:</strong>Ability to collaborate with data science teams and translate analytical models and prototypes into reliable, maintainable operational solutions.</li><li>
<strong>Monitoring and Observability:</strong>Familiarity with monitoring model performance, data drift, system reliability, and production workflows is beneficial.</li><li>
<strong>Responsible AI Practices:</strong>Knowledge of model governance, explainability, auditability, validation, risk management, and documentation practices for machine learning systems is an advantage.</li><li>
<strong>Collaboration and Communication:</strong>Strong interpersonal and communication skills, including the ability to explain technical concepts to business stakeholders and non-technical audiences.</li><li>
<strong>Independent Problem-Solving:</strong>Ability to work autonomously, deliver high-quality results, seek feedback when appropriate, and contribute to shared technical standards and best practices.</li><li>
<strong>Preferred Machine Learning Experience:</strong>Experience deploying, monitoring, or maintaining predictive models, forecasting systems, recommendation engines, natural language processing applications, classification models, AI agents, or decision-support tools.</li><li>
<strong>Preferred Business Applications:</strong>Experience supporting AI or machine learning solutions in operations, customer service, workforce management, CRM, business intelligence, or other business process environments.</li><li>
<strong>Preferred Cloud and MLOps Tools:</strong>Familiarity with infrastructure as code, cloud security, observability tools, model registries, workflow orchestration, and model monitoring platforms.</li><li>
<strong>Preferred Industry Knowledge:</strong>Familiarity with HR, payroll, human capital management (HCM), service operations, client retention, or voice-of-client analytics is an advantage.</li><li>
<strong>Physical and Technical Requirements:</strong>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.</li><li>
<strong>Remote Work Requirements:</strong>Ability to work effectively from a home office or another approved location within the United States and remain available during designated work hours.</li><h2>Benefits:</h2><li>
<strong>Annual Base Salary:</strong>$140,000–$163,000, depending on job-related knowledge, technical skills, professional experience, and market location.</li><li>
<strong>Performance-Based Compensation:</strong>Eligibility for quarterly bonuses and restricted stock unit grants based on individual performance.</li><li>
<strong>Employment Type:</strong>Full-time position.</li><li>
<strong>Remote Work:</strong>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.</li><li>
<strong>Healthcare Coverage:</strong>Medical, dental, and vision insurance.</li><li>
<strong>Financial Protection:</strong>Life insurance and disability coverage.</li><li>
<strong>Retirement Benefits:</strong>401(k) retirement plan with employer matching contributions.</li><li>
<strong>Family and Personal Support:</strong>Additional benefits and perks designed to support employees, their families, and financial well-being.</li><li>
<strong>Career Development:</strong>Opportunities to develop technical expertise, expand machine learning and cloud engineering skills, and contribute to innovative AI initiatives.</li><li>
<strong>Collaborative Environment:</strong>Work alongside data scientists, engineers, technology specialists, and business teams on impactful operational challenges.</li><li>
<strong>Innovation and Impact:</strong>Help implement practical AI solutions that improve business processes, workforce productivity, client outcomes, and operational decision-making.</li>