Sr Engineer, Data Science & Machine Learning Operations in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Sr Engineer, Data Science & Machine Learning Operations in United States.

This role sits at the intersection of data science, software engineering, and cloud operations, focused on delivering machine learning solutions that directly impact business performance. You will work closely with business stakeholders and technical teams to identify opportunities where ML can improve decisions, efficiency, and outcomes. The position emphasizes hands-on applied machine learning, using pragmatic, proven algorithms rather than overly complex research approaches. You will own the full lifecycle of ML models, from experimentation and feature engineering through production deployment and monitoring. The environment is highly collaborative, cloud-native, and centered on AWS-based data and ML platforms. Success in this role requires strong ownership, operational discipline, and a focus on measurable business value.

• Partner with business stakeholders to identify opportunities for machine learning and translate them into testable hypotheses and practical solutions that drive measurable outcomes.
• Design and execute experiments using applied ML techniques such as classification, regression, forecasting, clustering, and optimization.
• Own end-to-end model lifecycle including data preparation, feature engineering, training, deployment, monitoring, and iterative improvement based on real-world performance.
• Build and maintain production ML pipelines using AWS services such as SageMaker, including training workflows, endpoints, model registry, and monitoring systems.
• Operate and evolve data ingestion and transformation pipelines using tools such as AWS Glue, Step Functions, and EventBridge for batch and event-driven workloads.
• Manage S3-based data lake architecture with governed zones, Iceberg tables, and data cataloging using AWS Glue Data Catalog and Lake Formation.
• Implement observability, monitoring, and alerting across data and ML systems, including drift detection, data quality checks, and SLA tracking.
• Collaborate with infrastructure, security, and platform teams to ensure scalable, compliant, and secure cloud operations using infrastructure as code.
• Optimize performance and cost efficiency across data processing, training, storage, and inference workloads.
• Participate in incident response and root cause analysis for production issues affecting ML systems or data pipelines.

Requirements:

• 5+ years of professional experience in data science and machine learning, including building and deploying models in production environments.
• Strong hands-on experience with AWS ML and data services, especially SageMaker and related cloud-native tooling.
• Proven ability to translate business problems into analytical and machine learning solutions with measurable impact.
• Experience building and operating scalable data lakes and governed data platforms.
• Solid understanding of data engineering concepts, including ETL pipelines, streaming or event-driven architectures, and data modeling.
• Familiarity with infrastructure as code tools such as Terraform, CDK, or CloudFormation.
• Strong operational mindset with experience in CI/CD, monitoring, observability, and production incident handling.
• Pragmatic approach to machine learning, prioritizing reliability, scalability, and business value over unnecessary complexity.
• Excellent collaboration and communication skills with both technical and non-technical stakeholders.

Benefits:

• Competitive salary range of 165,000 to 200,000 USD, depending on experience and qualifications.
• Performance-based bonus potential.
• Comprehensive health coverage including medical, dental, and vision insurance.
• Retirement savings plan with employer contribution matching.
• Generous paid time off and flexible working arrangements.
• Access to nationwide gym and fitness memberships across thousands of locations.
• Supportive, modern work environment focused on innovation and impact.