• Build and/or lead the creation and maintenance of optimal global AI/ML architectures.

• Stay informed of industry trends and enable successful AI/ML solutions by leveraging best practices.

• Partnering effectively with stakeholders and business users.

• Participate in and set up Proof of Concepts (POCs) to demonstrate proposed solutions.

• Enable team members in the MLE space through training, culture, and team building.

• Identify , design, and implement internal process improvements: Automating manual processes, re-designing infrastructure for greater scalability, etc.

• Build infrastructure needed for AI/ML systems, such as model inference, automated (re-)training etc.

• Work with stakeholders including the Executive, Product, Data and Design teams to help with AI/ML-related technical issues and support their AI/ML infrastructure needs.

• Actively handle escalated incidents to resolution and suggest solutions to limit future exposure.

• Participate in Code Review and process improvement.

Qualifications and Experience

• Bachelor/Master/Engineering degree in IT/Computer Science/software engineering or relevant field .

• 8 + years of total experience in a complex, technical environment.

• Experience building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, or similarly advanced AI/ML problems.

• Experience one or more of the following AWS (Amazon Web Services) cloud services: Sage M aker, ECR/EC2 and Docker , AWS Batch processing, Lambda, Glue, EventBridge, etc.

• Experience with AI/ML operational tools such as SageMaker, Airflow, MLFlow , H2O, etc.

• Experience with developing production-grade Python code

• Experience with big data tools such as EMR (Elastic MapReduce) , Spark or similar.

• Experience with relational SQL and NoSQL databases.

• Experience leading, supporting, and working with cross-functional teams in a dynamic environment.