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Responsibilities
Key Responsibilities:
* Lead MLOps Strategy: Architect and implement machine learning pipelines capable of handling millions of customer predictions.
* Build Scalable Infrastructure: Design and build highly scalable and reliable cloud-based infrastructure on AWS to support the training, deployment, and monitoring of machine learning models at scale.
* Cost and Resource Management: Optimize the use of cloud resources, ensuring cost-effective scaling while maintaining high availability and performance standards.
* CI/CD Pipeline Implementation: Develop and optimize CI/CD pipelines specifically for ML models, ensuring smooth transitions from development to production.
* Automation and Optimization: Implement automation tools for model lifecycle management, model retraining, and data pipeline management.
* Model Monitoring and Performance: Oversee the development and implementation of robust monitoring solutions to track model performance, identify issues, and ensure that models continue to meet business objectives.
* Collaboration: Work closely with cross-functional teams to ensure alignment between business needs, model performance, and infrastructure capabilities.
* Documentation: Document processes, methodologies, and findings comprehensively and ensure that all documentation is kept up-to-date and accurate.
* Innovation and Research: Stay up to date with new machine learning techniques, tools, and technologies, and apply this knowledge to improve existing solutions.
Qualifications:
* Bachelor's, Master's, or PhD in Computer Science, Engineering, AI, or a related field.
* At least 5+ years of experience in machine learning operations.
* Extensive experience deploying, managing, and scaling AI/ML workloads for large-scale data on AWS services such as EC2, SageMaker, Lambda, and other AWS offerings.
* Proficiency in Docker, Kubernetes, and container orchestration, with experience in deploying machine learning models in these environments.
* Proven track record in designing and implementing CI/CD pipelines for ML models.
* Experience in performance tuning, cost optimization, and managing resources in cloud-based environments.
* Strong understanding of machine learning concepts, including supervised and unsupervised learning and deep learning.
* Strong programming skills in Python, and experience with frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Keras.
* Excellent problem-solving and analytical skills.
* Strong communication and collaboration skills.
* Nice to have: Prior experience working in a fast-paced startup environment.
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