Key Responsibilities
– 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)
