Machine Learning Engineer, AWS

This is a fully remote position, open to applicants in Oklahoma.

📋 Description

• Develop and sustain reproducible model training workflows on AWS utilizing SageMaker, S3, Glue, and associated services.

• Implement and manage real-time and batch inference services using CI/CD, versioning, along with canary, shadow, or A/B rollout strategies.

• Monitor production models for performance metrics, data drift, latency, and errors, while automating retraining triggers.

• Ensure model lineage, auditability, and traceability to meet compliance, governance, and reporting standards in the regulated gaming sector.

• Enforce least-privilege IAM, encryption, and secure data access methodologies across the machine learning platform.

• Enhance infrastructure and balance batch and real-time workloads to minimize platform costs without compromising reliability.

• Work collaboratively with engineers, data scientists, and product teams to convert business challenges into machine learning solutions.

• Investigate AWS services, machine learning frameworks, and deployment methodologies to boost reliability, observability, and developer productivity.

⛳️ Requirements

• A minimum of 3 years of experience in machine learning engineering, MLOps, or a closely related field.

• Practical experience with AWS ML and data services, including SageMaker, S3, Lambda, Step Functions, CloudWatch, and MWAA (Apache Airflow).

• Proficient in handling time-series data, encompassing feature engineering, seasonality management, and temporal train/test splits.

• Strong proficiency in Python and familiarity with ML frameworks such as scikit-learn, PyTorch, XGBoost, or their equivalents.

• Experience in constructing and maintaining CI/CD pipelines for machine learning systems.

• Capability to monitor and troubleshoot production ML systems for latency, drift, errors, and data quality, as well as to identify root causes.

• Comfortable with SQL and managing structured data at scale.

• Proven ability to collaborate across both technical and non-technical teams, and to communicate effectively.

• A history of self-directed learning and technical advancement in AWS, ML frameworks, or deployment methodologies.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Flexible work hours and remote work options.

• Comprehensive health and wellness benefits.

• Engaging work environment with a focus on innovation and collaboration.