Preparation for an AI Engineer role at Unisys should be rooted in your ability to articulate the "why" behind your technical decisions. We look for engineers who don't just build models, but build solutions that align with business goals.

Role-Related Knowledge – You should be prepared to discuss your past projects in depth, focusing on the specific libraries, frameworks, and methodologies you employed. Be ready to justify why you chose one approach over another.

Problem-Solving Ability – During your sessions, you will be expected to walk interviewers through your logic. We care less about memorizing textbook definitions and more about how you structure a solution to a novel or ambiguous technical challenge.

Culture Fit & Collaboration – Unisys thrives on cross-team synergy. Demonstrating that you are a team player who communicates clearly and welcomes feedback is as important as your technical proficiency.

The interview process at Unisys is designed to evaluate both your technical depth and your alignment with our collaborative culture. You can expect a multi-stage process that typically begins with a recruiter screen, followed by a combination of theoretical assessments and technical interviews. These sessions are intended to provide you with a comprehensive view of our team dynamics while allowing us to assess your fit for the specific challenges we are currently solving.

We value transparency and direct communication. Throughout the process, you will interact with various stakeholders, including hiring managers and lead engineers, to ensure that the role is a mutual fit. The pace is designed to be efficient, but you should expect to be challenged on your technical fundamentals and your ability to navigate project complexities.

The timeline above illustrates the progression from initial screening to final technical evaluation. Use this to pace your study schedule, ensuring you have enough time to review both your theoretical foundations and your past project experiences before the final rounds.

Technical Proficiency

We evaluate your command of core machine learning concepts and your ability to implement them.

Be ready to go over:

• Model Selection: Knowing when to use specific algorithms based on data constraints.
• Data Preprocessing: Handling missing values, normalization, and feature engineering.
• Evaluation Metrics: Understanding the business impact of your chosen metrics.

Example scenarios:

• "How would you optimize a model that is experiencing significant latency in production?"
• "Describe a time you had to troubleshoot a model that was performing well in testing but poorly in the wild."