Cloud Engineer - AI/ML

As a Cloud Engineer - AI/ML at Bright Vision Technologies, your primary responsibilities will include designing and deploying AI/ML workloads using AWS services like SageMaker and Lambda, optimizing AWS resources for deep learning environments, and developing serverless applications. You’ll also have the opportunity to collaborate with data scientists to enhance ML models, ensuring they perform effectively in a production setting.

To qualify for the Cloud Engineer - AI/ML role at Bright Vision Technologies, candidates should possess at least 10 years of experience in cloud engineering, particularly with AWS AI/ML services like SageMaker, Rekognition, and Lex. Proficiency in serverless technologies such as AWS Lambda and experience with big data processing tools are also essential. Additionally, having skills in Python and frameworks like TensorFlow or PyTorch will significantly enhance your suitability for this position.

Yes, Bright Vision Technologies encourages applicants from all over the USA for the Cloud Engineer - AI/ML position. We recognize the value of diverse talent regardless of geographic location, and we are committed to providing flexible opportunities for qualified candidates, including those seeking H1B transfers.

In addition to the required skills for the Cloud Engineer - AI/ML role at Bright Vision Technologies, preferred skills include an AWS Certified Machine Learning – Specialty certification, experience with Edge AI technologies like AWS Greengrass, and proficiency in Kubernetes for managing machine learning workloads. Having these skills will give you an edge in this competitive position.

To learn more about the Cloud Engineer - AI/ML position, you can reach out directly by sending your resume to madhavi@bvteck.com or by calling +1 (908) 505 3454. Our team is dedicated to providing all the information you need regarding this exciting opportunity!

When answering this question, focus on specific AWS AI/ML services you've used, such as SageMaker and Rekognition. Describe how you've deployed machine learning models and optimized resources in AWS environments. Highlight any projects where you made significant contributions, emphasizing your role in enhancing the performance of those models.

Outline your experience with various AWS resource types, especially GPU instances for deep learning tasks. Discuss how you assess workload requirements, choose instance types, and scale resources to ensure cost-effectiveness and performance. Share examples from past experiences that demonstrate your troubleshooting skills and ability to optimize resources.

Communicate your strategies for effective collaboration, such as holding regular meetings, sharing tools, and leveraging feedback. Discuss any specific tools or practices you adopt to ensure that data scientists' insights are integrated into the model optimization process. Providing real-world examples will illustrate your teamwork skills.

Share specific instances where you've utilized AWS Lambda to develop serverless applications. Discuss your understanding of its event-driven architecture, and describe how you’ve implemented Lambda in combination with services like API Gateway and Step Functions. Providing specific project outcomes will enhance your answer.

Reflect on a few specific challenges, such as scaling issues, resource limitations, or model performance problems. Describe how you assessed the situation, the strategies you employed to address those issues, and the ultimate outcomes. This showcases your problem-solving skills and adaptive thinking.

Discuss your commitment to continuous learning, such as attending industry conferences, subscribing to relevant publications, and taking online courses. You might also mention any professional networks or communities you engage in to exchange knowledge and best practices within the fields of cloud computing and AI/ML.

Frame your answer around a specific project where you successfully implemented AWS Glue, EMR, or Spark. Describe your role, the objectives of the project, the technologies used, and the impact it had on the organization. Highlight your adaptability and technical skills throughout the project.

Explain the importance of cloud security and detail specific measures you implement, such as IAM roles, VPC configurations, and encryption strategies. Share any experiences where you had to address security vulnerabilities or build secure architectures, which demonstrate your proactive approach to cloud security.

Describe your systematic methodology for debugging, such as logging, tracing, and monitoring. Discuss specific tools and techniques you've used in the AWS ecosystem, like CloudWatch or X-Ray, and provide an example illustrating how you effectively identified and resolved a significant issue in a cloud application.

Discuss Kubernetes' capacity to orchestrate containers for machine learning workflows, citing your experience with it during the deployment or scaling of services. Provide insights into how Kubernetes enhances flexibility and resource management for ML workloads, and illustrate your perspective with practical examples from your work.