Senior Machine Learning Engineer_W2 ONLY
Role: Senior Machine Learning Engineer
Location: onsite (4-5 days) – Charlotte, NCDuration: 6+ months
Senior Machine Learning Engineer with deep, hands-on experience delivering end-to-end ML solutions, including model design, optimization, deployment, and automation using AWS SageMaker.
Key Responsibilities:
Build and deploy scalable ML models to solve real-world business problems
Own the full ML lifecycle: data exploration, feature engineering, model prototyping, validation, and productionization
Optimize and automate workflows using best practices and MLOps principles
Collaborate cross-functionally with data scientists, engineers, and business stakeholders
Translate complex ML concepts into clear, actionable insights
Mentor team members and guide best practices across projects
Requirements:
Expert in Python, SQL, and modern ML toolkits (e.g., scikit-learn, XGBoost)
Extensive hands-on experience with AWS SageMaker (training, tuning, endpoints, pipelines)
Strong understanding of model evaluation, tuning, and production deployment
Proven ability to work across large, complex datasets and cloud-based infrastructures
Bonus: Familiarity with Spark, Hive, and data governance frameworks
Job Type: Contract
Pay: $40.00 - $45.00 per hour
Expected hours: 40 per week
Schedule:
8 hour shift
Day shift
Work Location: In person
Role: Senior Machine Learning Engineer
Location: onsite (4-5 days) – Charlotte, NCDuration: 6+ months
Senior Machine Learning Engineer with deep, hands-on experience delivering end-to-end ML solutions, including model design, optimization, deployment, and automation using AWS SageMaker.
Key Responsibilities:
Build and deploy scalable ML models to solve real-world business problems
Own the full ML lifecycle: data exploration, feature engineering, model prototyping, validation, and productionization
Optimize and automate workflows using best practices and MLOps principles
Collaborate cross-functionally with data scientists, engineers, and business stakeholders
Translate complex ML concepts into clear, actionable insights
Mentor team members and guide best practices across projects
Requirements:
Expert in Python, SQL, and modern ML toolkits (e.g., scikit-learn, XGBoost)
Extensive hands-on experience with AWS SageMaker (training, tuning, endpoints, pipelines)
Strong understanding of model evaluation, tuning, and production deployment
Proven ability to work across large, complex datasets and cloud-based infrastructures
Bonus: Familiarity with Spark, Hive, and data governance frameworks
Job Type: Contract
Pay: $40.00 - $45.00 per hour
Expected hours: 40 per week
Schedule:
8 hour shift
Day shift
Work Location: In person
