What are the responsibilities and job description for the Senior Machine Learning Engineer with AWS SageMaker - Need 12+ years (Only On-Site) position at DataFactZ?
Job Details
Job Role: Senior Machine Learning Engineer
Location onsite (4-5 days) Charlotte, NC
Type: Any visa type
Duration: 6 months
For the client, we need a 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
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