
Software Architect
3 weeks ago
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)
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