Senior Machine Learning Ops Engineer

Senior Machine Learning Ops Engineer

Durham, NC

Type:
Contract

Category:
Data

Industry: Financial Services

Hybrid Every other week onsite/5 days in either Durham, NC, Jersey City, NJ or Westlake, TX

Our client’s enterprise data science platform team builds advanced cloud and software solutions to package, deploy, and scale AI/ML models in production. The team collaborates across business and engineering groups to deliver reliable infrastructure, analytics tooling, and operational capabilities that power model-driven customer experiences in financial services.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $65.00 to $80.00/hr. w2

• Create frameworks to support ML infrastructure and pipelines, including containerization and deployment of ML models.
• Extend ML platforms to scale model training, deployment, and inference.
• Partner with data scientists to enable model development, training, and operationalization on the foundational platform.
• Operationalize ML models at scale to serve high-volume predictions.
• Design and develop a feature generation and feature store framework to promote reuse across models.
• Build tools to detect data and feature shifts, monitor model uncertainty, and automate prediction explanations for diagnostics.
• Explore and leverage emerging technology trends to simplify the data and ML ecosystem.
• Drive innovation, guide teams to improve development agility and productivity, and resolve technical roadblocks.
• Deliver system automation by establishing CI/CD pipelines.

• Strong object-oriented Python development experience.
• Hands-on AWS experience with services such as Sage Maker, Bedrock, S3, Cloud Formation, SNS, SQS, Lambda, AWS Batch, Step Functions, Event Bridge, and Cloud Watch.
• CI/CD expertise with Jenkins and Git, including automated build and deployment pipelines.
• Containerization experience with Docker for building and deploying applications.
• Infrastructure as Code proficiency with AWS Cloud Formation and tools such as Terraform or Open Tofu.
• Extensive experience deploying, tuning, monitoring, and measuring machine learning models in production.
• Experience building scalable distributed systems using open-source technologies.
• 5+ years developing Python-based cloud applications and/or ML solutions.
• 1+ years building ML infrastructure and MLOps on AWS Sage Maker.
• Experience hosting applications on Kubernetes environments, including EKS.
• Proficiency with Python ML ecosystem tools such as numpy, pandas, scikit-learn, and tensor flow, plus Linux scripting.
• Ability to design software using object-oriented and functional paradigms; basic knowledge of Java and Groovy is a plus.
• Experience with Agile methodologies including Kanban and Scrum.
• SQL database skills (preferred).
• Azure Cognitive Services and Google Vertex AI familiarity (preferred).

• Bachelor’s or Master’s degree in a technology-related field such as Computer Science or Engineering.
• AWS certification such as Developer, Solutions Architect, or Machine Learning Specialty
• Kubernetes certification