Machine Learning Operations Engineer

Imaginesoftware; Charlotte (NC); On-site; USD 120,000 - 170,000; Full time

Job summary

ImagineSoftware in Charlotte, NC seeks a Machine Learning Operations Engineer to bring machine learning models into reliable production environments supporting healthcare solutions. You will define the MLOps strategy, architect scalable pipelines, and collaborate with data science and leadership to drive governance and experimentation.

The role requires 3+ years in MLOps, deep AWS expertise, Python proficiency, and strong CI/CD/DevOps skills.

Qualifications

• 3+ years hands-on experience in MLOps or production ML systems.
• Deep expertise in AWS cloud services (SageMaker, EKS, EC2, S3, IAM, CloudWatch).
• Strong programming skills in Python (and ideally another language).
• Experience building and maintaining ML pipelines and orchestration tools (Airflow, Kubeflow, Step Functions).
• Familiarity with streaming/data processing frameworks (Kafka, Spark).
• Experience with feature stores, model registries, and experiment tracking tools (MLflow, Feast).
• Knowledge of model governance, fairness, and explainability frameworks.

Responsibilities

• Define and lead the MLOps vision, architecture, and roadmap across model development and production environments.
• Design end-to-end ML pipelines from data ingestion to deployment and monitoring.
• Establish scalable patterns for model lifecycle management (versioning, retraining, rollback, auditability).
• Partner with Data Science teams to productionize models reliably and efficiently.
• Implement CI/CD pipelines for ML workflows, including automated testing and deployment.
• Enable real-time and batch inference systems with high availability and low latency.

Skills

MLOps

AWS Cloud

Python

CI/CD

Model governance

Data drift monitoring

Education

BS in CS/Engineering/Data Science

Tools

Airflow

Kubeflow

MLflow

Feast

SageMaker

EKS

Step Functions

Kafka

Spark

Job description

Machine Learning Operations EngineerHow this Role Makes an ImpactThe 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 DutiesMLOps Strategy & ArchitectureWork with senior leadership to define and lead the MLOps vision, architecture, and roadmap across model development and production environmentsDesign end-to-end ML pipelines, from data ingestion and feature engineering to training, validation, deployment, and monitoringEstablish scalable patterns for model lifecycle management (versioning, retraining, rollback, auditability)Model Development EnablementPartner with Data Science teams to productionize models efficiently and reliablyBuild and maintain tooling for experimentation, model tracking, and reproducibility (e.g., feature stores, experiment tracking systems)Optimize training workflows for performance, cost, and scalabilityProduction Infrastructure & DeploymentArchitect 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 deploymentEnable real-time and batch inference systems with high availability and low latencyMonitoring, Reliability & GovernanceEstablish monitoring for model performance, data drift, and system healthImplement alerting, logging, and observability frameworksEnsure compliance with security, privacy, and governance standardsOther duties as assignedEducation and/or Experience NeededBS in computer science, engineering, data science, or related technical field5+ years of experience in machine learning engineeringQualifications You Must Have3+ years of hands-on experience in MLOps or production ML systemsDeep 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 principlesExperience 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 systemsKnowledge of model governance, fairness, and explainability frameworksEmployment TypeFull Time, ExemptReporting StructureSr. Vice President, Data ScienceLocation & Work AuthorizationCharlotte, 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.