Software Architect

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)

#J-18808-Ljbffr