Senior Data Engineer,MLops (Remote- US)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Data Engineer, MLOps in the United States.

This role offers an exciting opportunity to lead the development and deployment of robust machine learning infrastructure in a fast-paced, innovative environment. You will partner with data scientists, engineers, and product teams to operationalize data-driven solutions, streamline ML pipelines, and ensure high-quality model delivery. The position focuses on end-to-end MLOps, including automation, feature store management, real-time inference, monitoring, and CI/CD integration. You will design scalable, resilient systems that accelerate time-to-market for ML models, while implementing best practices for governance, reproducibility, and reliability. This role is ideal for a technically strong engineer who thrives in collaborative, high-impact environments and enjoys solving complex ML engineering challenges.

Accountabilities:

• Design, implement, and maintain ML pipelines and data engineering workflows using AWS services such as SageMaker, Step Functions, and EKS.
• Operationalize key data science solutions for underwriting, pricing, claims routing, and marketing applications.
• Build and manage a shared feature store supporting both batch and real-time feature retrieval.
• Develop real-time inference services with low-latency endpoints and deploy using blue/green or canary strategies.
• Implement testing, monitoring, and validation strategies within CI/CD pipelines to ensure platform reliability and performance.
• Enable ML governance, including model versioning, experiment tracking, reproducibility, and automated retraining based on drift or business events.
• Collaborate with data scientists, engineers, and cross-functional teams to deliver scalable, production-ready ML solutions.

• Bachelor’s degree or equivalent relevant experience.
• Minimum 8 years of industry experience, with at least 2 years in MLOps and 2 years in software engineering.
• Proficiency in Python and Docker; familiarity with build tools such as Bash and Bazel.
• Strong experience in IaC principles and tools, particularly Terraform.
• Demonstrated expertise in designing, deploying, and managing scalable MLOps solutions on AWS.
• Experience with the full ML lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring.
• Proficiency in designing workflows using tools like AWS Step Functions.
• Experience implementing CI/CD pipelines tailored for machine learning systems.
• Strong collaborative skills and excellent written and verbal communication.
• Bonus: experience in distributed systems, complex APIs, Snowflake Snowpark, or regulated industries like insurance.