Location: Reading, PA
Salary: $60.00 USD Hourly - $65.00 USD Hourly
Description: Hybrid ML Ops Engineer - AI Platform & Machine Learning Operations Location: Hybrid (Reading, PA) - 3 days onsite / 2 days remote
About the AI Platform This organization's AI platform is an industry-first system that leverages billions of data points, advanced machine learning models, and deep domain expertise to deliver real-time benchmarking, diagnostics, and performance insights. The platform processes
100B+ data points annually and operates
300+ real-time ML models, powering high-impact intelligence across multiple enterprise functions.
Role Overvie As a
Hybrid ML Ops Engineer, you will play a key role in operationalizing and scaling machine learning systems across the enterprise. You'll collaborate closely with data scientists, ML engineers, platform engineers, and software development teams to build and maintain high-availability ML infrastructure that supports mission-critical AI initiatives.
This role provides broad cross-functional exposure, high visibility, and the opportunity to advance the organization's MLOps maturity.
Responsibilities MLOps & Model Deployment - Deploy and operationalize ML models with strong focus on auditability, versioning, reproducibility, and security.
- Transform offline, research-oriented models into robust, production-grade ML systems.
- Support scheduled data processing jobs (weekly/monthly), including participation in an on-call rotation.
AI Platform Engineering - Support enterprise ML platforms such as AWS SageMaker, SAS Viya, and Dataiku.
- Build scalable pipelines for model training, inference, and performance monitoring.
- Design and maintain data pipelines and engineering infrastructure that power large-scale AI applications.
Collaboration & Cross-Team Enablement - Partner with data scientists to optimize feature engineering, model training workflows, and pipeline performance.
- Support Self-Service AI initiatives and enable citizen-data-scientist communities.
- Communicate complex ML and data engineering concepts to both technical and non-technical audiences.
- Contribute to multiple AI, data, and platform initiatives across the organization.
Software Engineering - Develop backend services and microservices that integrate AI insights into internal and customer-facing applications.
- Apply strong engineering practices: automated testing, documentation, CI/CD, code reviews, and automation.
- Build monitoring, alerting, and diagnostic tooling to ensure pipeline reliability and system observability.
Required Qualifications Education & Experience - Bachelor's degree in Computer Science, Computer Engineering, or related field with 5-7 years of industry experience, or
- Master's degree with 3+ years of industry experience.
Technical Skills - Strong Python expertise for ML pipelines, training jobs, and automation tooling.
- Experience with DevOps practices and CI/CD workflows.
- Hands-on experience with AWS (SageMaker, EC2, S3, IAM).
- Experience building and consuming REST APIs and backend systems (Java, Python, Spring, Angular, React).
- Familiarity with relational databases (MySQL), NoSQL systems, and data engineering practices.
Preferred Qualifications - Experience with MLOps platforms such as SageMaker, SAS Viya, or Dataiku.
- Exposure to data governance frameworks.
- Experience with model monitoring, drift detection, and ML observability.
- Familiarity with MLflow, Kubeflow, or similar MLOps ecosystems.
Soft Skills - Excellent written and verbal communication; able to break down ML concepts for diverse audiences.
- Strong collaboration skills with cross-functional engineering and analytics teams.
- Ability to support mission-critical systems while managing multiple concurrent projects.
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