Machine Learning Operations Engineer at ImagineSoftware™

Job description

Machine Learning Operations Engineer How this Role Makes an Impact The Imagine team is a growing company, and we are inviting a motivated Machine Learning Operations Engineer to join our talented team! This candidate will play a critical role in bringing machine learning models into reliable, scalable production environments that support innovative healthcare solutions. The ideal candidate will combine strong technical expertise with a solutions-oriented mindset. If you are looking for a place that offers a challenging and fast-paced environment with the opportunity to grow and develop, look no further! We are always looking for quality people to join our growing team. Must-haves include the ability to adapt to an ever-changing environment, work quickly and efficiently, continuously challenge the status quo, and be an innovative and solutions-based thinker. Essential Duties MLOps Strategy & Architecture • Work with senior leadership to define and lead the MLOps vision, architecture, and roadmap across model development and production environments • Design end-to-end ML pipelines, from data ingestion and feature engineering to training, validation, deployment, and monitoring • Establish scalable patterns for model lifecycle management (versioning, retraining, rollback, auditability) Model Development Enablement • Partner with Data Science teams to productionize models efficiently and reliably • Build and maintain tooling for experimentation, model tracking, and reproducibility (e.g., feature stores, experiment tracking systems) • Optimize training workflows for performance, cost, and scalability Production Infrastructure & Deployment • Architect and manage cloud-native ML infrastructure in AWS (e.g., SageMaker, EKS, Lambda, S3, Step Functions) • Implement CI/CD pipelines for ML workflows, including automated testing, validation, and deployment • Enable real-time and batch inference systems with high availability and low latency Monitoring, Reliability & Governance • Establish monitoring for model performance, data drift, and system health • Implement alerting, logging, and observability frameworks • Ensure compliance with security, privacy, and governance standards Other duties as assigned Education and/or Experience Needed • BS in computer science, engineering, data science, or related technical field • 5+ years of experience in machine learning engineering Qualifications You Must Have • 3+ years of hands-on experience in MLOps or production ML systems • Deep expertise in AWS cloud services (e.g., SageMaker, EKS, EC2, S3, IAM, CloudWatch) • Strong programming skills in Python (and ideally one additional language such as Java) • Experience building and maintaining ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, Step Functions) • Strong understanding of CI/CD practices and DevOps principles • Experience with feature stores, model registries, and experiment tracking tools (e.g., MLflow, Feast) • Familiarity with streaming/data processing frameworks (e.g., Kafka, Spark) • Experience supporting both real-time and batch ML systems • Knowledge of model governance, fairness, and explainability frameworks Employment Type Full Time, Exempt Reporting Structure Sr. Vice President, Data Science Location & Work Authorization Charlotte, NC is the preferred location for this position. This position is not eligible for H-1B visa sponsorship or C2C (Corp-to-Corp) arrangements. Candidates must be authorized to work in the United States without employer sponsorship, now or in the future. At ImagineSoftware, we have a role to play in contributing to an inclusive world. We work every day to lead with our values and beliefs that enable you to develop your potential and bring your full self to the workplace. Our culture of diversity and inclusion enables more creative thinking and better ideas for addressing a more diverse market. We hire driven people from all backgrounds because it makes us a great company, and because it’s the right thing to do. If you share these values, you will find a home at ImagineSoftware.

Requirements

Must-haves

Machine Learning Operations Engineer
How this Role Makes an Impact
MLOps Strategy & Architecture
Work with senior leadership to define and lead the MLOps vision, architecture, and roadmap across model development and production environments
Design end-to-end ML pipelines, from data ingestion and feature engineering to training, validation, deployment, and monitoring
Establish scalable patterns for model lifecycle management (versioning, retraining, rollback, auditability)
Model Development Enablement
Partner with Data Science teams to productionize models efficiently and reliably
Build and maintain tooling for experimentation, model tracking, and reproducibility (e.g., feature stores, experiment tracking systems)
Optimize training workflows for performance, cost, and scalability
Production Infrastructure & Deployment
Architect and manage cloud-native ML infrastructure in AWS (e.g., SageMaker, EKS, Lambda, S3, Step Functions)
Implement CI/CD pipelines for ML workflows, including automated testing, validation, and deployment
Enable real-time and batch inference systems with high availability and low latency
Monitoring, Reliability & Governance
Establish monitoring for model performance, data drift, and system health
Implement alerting, logging, and observability frameworks
Ensure compliance with security, privacy, and governance standards
Other duties as assigned
Education and/or Experience Needed
BS in computer science, engineering, data science, or related technical field
5+ years of experience in machine learning engineering
Qualifications You Must Have

Nice-to-haves

Charlotte, NC is the preferred location for this position.

Application prep

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Keywords to mirror

machine learningmlawsci/cdpythonjava

Platform: paycom
Effort: medium

Benefits

Benefits detected in the listing.

Vision