Educational requirements: Bachelor
English requirements: Competent English
Requirements for skilled employment experience for years: 1-3 years
Required residence status: Temporary visa, Permanent resident, Citizen
Accept remote work: unacceptable
Technical Architect is will be will enjoy taking ownership of the strategic delivery of technology architecture strategy including designing, refining and uplifting system architecture to meet the expectation of customers. Your role will provide a critical capability in helping technology delivery teams understand how their program progress fits into the wider group view and on how to best structure Ab Initio technology solutions to stay future fit, compliant and best in market.
Roles and Responsibilities • Develop, implement and support end-to-end MLOps pipelines on the AWS SageMaker platform, including data ingestion, preprocessing, feature engineering, model training, validation, deployment, and monitoring. • Collaborate with cross-functional teams, including data scientists, software engineers, and DevOps engineers, to ensure seamless integration of machine learning models into production systems. • Work with data scientist team to define Amazon SageMaker pipelines to integrate ML model development steps. • Develop and maintain standard framework to implement BYOM (Bring Your Own Model) • Implement and maintain continuous integration and continuous deployment (CI/CD) pipelines. • Implementing Amazon SageMaker endpoint configuration and Application integration using Amazon API Gateway, AWS Lambda etc. • Monitor and optimize model performance using Amazon SageMaker Model Monitoring offerings and define strategies for model retraining, hyperparameter tuning, and model versioning. • Ensuring that the ML models are developed aligning to AWS ML well architected framework and Responsible AI frameworks.
Education Qualification, Experience and Expertise • Bachelor’s or master’s degree in computer science, Engineering, or a related field. • Strongs experience in developing and deploying machine learning models on the Amazon SageMaker Endpoints. • Having AWS Certification (AWS Certified Machine Learning Specialty) is preferred. • Well versed with Real time and Batch mode predictions and inferences. • Should be comfortable with the Bias/Explanability, Data/Model drift concepts. • Must have hands on Experience on implementing Amazon SageMaker Model Monitoring pipelines. • Understanding of H2O.ai MLOps offerings and how Amazon Sagemaker and H2O integration works. • Good to have experience on AWS AI services like Amazon Lex, Transcribe, Comprehend etc. • Sound understanding of MLOps principles and best practices, including experience with Docker, Kubernetes, and version control systems such as Git. • Mush have experience with scripting and programming languages like Python, as well as familiarity with libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn. • Strong knowledge of software development methodologies and CI/CD pipelines, including experience with Jenkins, GitLab, or similar tools. • Excellent problem-solving and analytical skills
