Design and develop scalable, high-performance software architectures that leverage cloud-native technologies
Lead the architectural design of software solutions, ensuring alignment with business requirements and technology strategy
Design and develop scalable, high-performance cloud architectures tailored for machine learning workloads, including model training, deployment, and monitoring
Collaborate with data scientists and ML engineers to understand model requirements and optimize training and inference workflows
Evaluate and select appropriate cloud services (e.g., AWS Sagemaker, Azure ML, Google AI Platform) to optimize performance, cost, and scalability
Ensure the security, availability, and integrity of cloud-based ML applications and data
Conduct architectural reviews, code reviews, and provide technical leadership and guidance to development and data science teams
Stay up-to-date with emerging cloud and ML technologies and industry trends to make informed architectural decisions
Document architectural decisions, designs, and guidelines for implementation teams
Qualifications and Skills
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field
Proven experience as a Software Architect or Senior Developer with at least 10 years of technology expertise
Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud, including machine learning and data processing services
Hands-on experience with cloud-based ML services (e.g., AWS Sagemaker, Azure Machine Learning, Google AI Platform)
Proficiency in building ML pipelines using cloud-native architectures, containerization (e.g., Docker, Kubernetes), and serverless computing
Solid programming skills in Python, Java, or other relevant languages used in ML development
In-depth knowledge of software design patterns, architectural principles, and best practices
Strong problem-solving skills with the ability to work in a fast-paced, agile environment
Excellent communication and leadership abilities to collaborate effectively with stakeholders, data scientists, and development teams
Certifications in cloud technologies (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, Google Cloud Architect)
Familiarity with data processing and storage solutions for ML workloads, such as BigQuery, Data Lake, or Data Warehouse solutions
Knowledge of security and compliance requirements for cloud-based ML applications
Experience with version control and model tracking tools (e.g., MLflow, DVC)
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