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.