About the job
Tiger Analytics is a premier analytics consulting firm renowned for empowering Fortune 100 companies to unlock business value from their data. Our team of consultants boasts extensive expertise in Data Science, Machine Learning, and Artificial Intelligence, earning accolades from industry-leading research firms such as Forrester and Gartner.
We are on the lookout for an enthusiastic and skilled Machine Learning Operations Architect to join our dynamic team.
Job Overview:
As a Senior Machine Learning Operations Architect, you will collaborate with a talented group of Machine Learning Engineers to support, construct, and enhance machine learning capabilities throughout the organization. Your work will involve close interactions with internal customers and infrastructure teams to develop cutting-edge data science workbenches, ML platforms, and products. This role offers an opportunity to deepen your expertise in modern Machine Learning frameworks, libraries, and technologies while addressing the evolving needs of the business. If you thrive on innovative solutions and enjoy working in a collaborative, hands-on environment, this position is perfect for you.
Key Responsibilities:
- Design and implement scalable and reliable systems utilizing cloud-based architectures, technologies, and platforms for high-volume model inference.
- Deploy and manage machine learning and data pipelines within production settings.
- Engage in containerization and orchestration strategies for effective model deployment.
- Participate in rapid iteration cycles, adjusting to changing project requirements.
- Work as part of a cross-functional Agile team to develop and enhance software that supports advanced big data and ML applications.
- Apply CI/CD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
- Maintain rigorous code management to minimize vulnerabilities, ensuring models are governed effectively and adhere to Responsible and Explainable AI standards.
- Collaborate with data scientists, software engineers, and other stakeholders to establish best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting, and automated model deployment.
- Oversee machine learning infrastructure, ensuring optimal availability and performance.
- Implement comprehensive monitoring and logging solutions for model performance and system health.
- Continuously monitor the real-time performance of deployed models, analyze data, and proactively resolve performance issues to guarantee optimal model functioning.
- Troubleshoot and resolve production issues pertaining to ML model deployment and performance.

