Machine Learning Engineer

Founded in 2002, AU10TIX is the global leader in AI driven identity verification and management, protecting the world’s largest brands against advanced fraud. The company’s future-proof product portfolio helps businesses provide frictionless customer onboarding and verification in 4-8 seconds while staying ahead of emerging threats and evolving regulatory requirements.

We are seeking a proactive and collaborative Machine Learning Engineer / Data scientist to join our growing team. This role is ideal for someone with a solid foundation in machine learning and data science, as well as a passion for building scalable, production-ready ML systems. You’ll work closely with other data scientists, engineers, and product teams to develop and deploy models that drive real business impact, and report to the Data Engineering team leader.

• Design, develop machine learning models.
• Build and maintain MLOps pipelines to streamline model lifecycle management.
• Collaborate with cross-functional teams to understand business needs and translate them into ML solutions.
• Monitor model performance and retrain/update models as needed.
• Share knowledge and mentor other team members to foster a culture of continuous learning.
• Write clean, maintainable, and well-documented code.
• Contribute to data infrastructure and ensure data quality and accessibility.

Requirements:

• 3 years of hands-on experience in machine learning engineering, data science, or a similar role.
• Strong knowledge of machine learning algorithms, model evaluation, and feature engineering.
• Proficiency in Python and ML libraries.
• Solid understanding of SQL and experience working with relational databases.
• Familiarity with cloud platforms (preferably Azure).
• Excellent communication skills and a team-first mindset.
• A “can-do” attitude with a willingness to take ownership and drive projects forward.
• Ability to work independently and be receptive to feedback.
• Passion for sharing knowledge and contributing to team growth.
• A bachelor's degree in a quantitative field or equivalent experience (a master's degree is an advantage).

Nice to Have

• Experience with CI/CD tools (such as GitHub) and MLOps platforms (e.g., Azure ML Studio - Advantage).
• Exposure to real-time data processing (e.g., Event Hub, Kafka, Spark Streaming).
• Understanding data privacy and model fairness principles.
• Experience in Identity Verification or fraud detection/