EY - GDS Consulting - AI and DATA -MLOPS - Senior

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

ML Ops Engineer (Senior Consultant)
 

Key Responsibilities:

• Lead the design, implementation, and maintenance of scalable ML infrastructure.
• Collaborate with data scientists to deploy, monitor, and optimize machine learning models.
• Automate complex data processing workflows and ensure data quality.
• Optimize and manage cloud resources for cost-effective operations.
• Develop and maintain robust CI/CD pipelines for ML models.
• Troubleshoot and resolve advanced issues related to ML infrastructure and deployments.
• Mentor and guide junior team members, fostering a culture of continuous learning.
• Work closely with cross-functional teams to understand requirements and deliver innovative solutions.
• Drive best practices and standards for ML Ops within the organization.
   

Required Skills and Experience:

• Minimum 5 years of experience in infrastructure engineering.
• Proficiency in using EMR (Elastic MapReduce) for large-scale data processing.
• Extensive experience with SageMaker, ECR, S3, Lamba functions, Cloud capabilities and deployment of ML models. 
• Strong proficiency in Python scripting and other programming languages.
• Experience with CI/CD tools and practices.
• Solid understanding of the machine learning lifecycle and best practices.
• Strong problem-solving skills and attention to detail.
• Excellent communication skills and ability to work collaboratively in a team environment.
• Demonstrated ability to take ownership and drive projects to completion.
• Proven experience in leading and mentoring teams.
   

Beneficial Skills and Experience:

• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with data visualization tools and techniques.
• Knowledge of big data technologies (Spark, Hadoop).
• Experience with version control systems (Git).
• Understanding of data governance and security best practices.
• Experience with monitoring and logging tools (Prometheus, Grafana).
• Stakeholder management skills and ability to communicate technical concepts to non-technical audiences.

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