AI/ML Engineer with AWS SageMaker
About The Position
We are seeking a highly experienced AI/ML Engineer with deep expertise in AWS SageMaker , end-to-end machine learning pipeline development, and strong proficiency in Python or R . In this role, you will architect, build, deploy, and optimize scalable machine learning solutions for complex business problems across our U.S. teams. You will collaborate with cross-functional stakeholders—including data scientists, software engineers, product managers, and cloud engineering teams—to deliver robust ML platforms and cutting-edge AI models in a production environment.
Requirements
- 10+ years of experience in Machine Learning, Data Science, or AI engineering roles.
- Advanced proficiency in Python or R (Python strongly preferred).
- Hands-on experience with AWS SageMaker (training jobs, endpoints, pipeline automation, feature store, model registry).
- Strong background in ML model development: regression, classification, time-series, NLP, deep learning, etc.
- Experience working with AWS cloud services such as S3, Lambda, ECS/EKS, Glue, Redshift, IAM, CloudWatch.
- Proven experience building and scaling ML pipelines in production environments.
- Skilled in using ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.).
- Strong understanding of MLOps practices, automated deployment, containers, and versioning.
- Experience with REST APIs, microservices, and containerization (Docker, Kubernetes).
- Excellent communication and stakeholder management skills.
Responsibilities
- Design, develop, and deploy machine learning models using AWS SageMaker (training, hosting, pipelines, model registry).
- Build and optimize end-to-end ML pipelines , including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
- Implement automation for CI/CD of ML solutions using tools such as SageMaker Pipelines , AWS CodePipeline , CodeBuild , or similar.
- Collaborate with data engineering teams to build scalable data architectures (Lake Formation, Glue, EMR, Redshift, etc.).
- Develop high-quality, reusable, and modular ML code in Python or R .
- Optimize model performance, inference latency, cost efficiency, and monitoring in production.
- Maintain and improve MLOps best practices, including model governance, versioning, and reproducibility.
- Work with distributed systems and large-scale datasets for training and inference.
- Evaluate new AI/ML technologies, frameworks, and cloud capabilities to enhance the ML platform.
- Drive technical leadership, mentorship, and thought leadership across AI/ML teams.
Benefits
- Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
