Senior Machine Learning Engineer

What success looks like in this role:

We are collaborating with Fortune 500 companies to revolutionize their employees' workplace experiences using Generative AI-powered Enterprise Personal Assistants. In this role, you'll be at the forefront of innovation with Large Language Models (LLMs), driving significant advancements in this exciting field.

• Operationalize Machine Learning (ML) solutions, transforming theoretical models into practical applications.

• Design, develop, and implement scalable machine learning models and systems, ensuring robustness, performance, and maintainability.

• Actively contribute to Machine Learning feasibility studies, evaluating potential solutions and their applicability.

• Collaborate cross-functionally with software engineers, data scientists, product managers, and other stakeholders to integrate ML models into production systems seamlessly.

• Regularly participate in bi-weekly meetings focused on reviewing recent ML papers, contributing insights, and staying abreast of current trends.

• Monitor and optimize the performance of ML models in production, ensuring they meet business objectives and deliver accurate, reliable results.

• Conduct data analysis and preprocessing, preparing large datasets for model training, cleaning data, and selecting appropriate features for various ML algorithms.

• Contribute to the research and experimentation of new machine learning techniques and frameworks, particularly in the areas of deep learning, reinforcement learning, and LLMs.

• Ensure ML solutions meet security and compliance requirements, including responsible AI practices and data privacy standards.

• Mentor junior team members, providing technical guidance, reviewing code, and fostering knowledge-sharing to elevate the team's overall capabilities.

You will be successful in this role if you have:

• Minimum 5 years of experience in Software Engineering or Machine Learning Engineering.

• Proven experience working on LLM-based applications, demonstrating a deep understanding of their development and operationalization.

• Extensive experience with programming languages, particularly Python and SQL.

• Profound knowledge of algorithms and data structures.

• A solid background in statistics and/or mathematics.

• Familiarity with at least one major cloud service provider (Azure, Amazon Web Services, or Google Cloud).

• Practical experience with Apache Spark.

• Quick learner with the ability to grasp new technologies swiftly.

• Strong capability to work effectively and independently, as well as collaboratively in a team setting.

• Excellent communication skills, both written and verbal, in English

• What do we offer?

• Hybrid working is supported

• Monthly gross 35.000 HUF cafeteria - regardless of your weekly working hours

• Private Health Insurance - Premium Package provided for all Unisys associates after 1-year tenure 

• Home office allowance

• Last and foremost, a great team, also a lot of learning and training opportunities

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Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.

This commitment includes our efforts to provide for all those who seek to express interest in employment the opportunity to participate without barriers. If you are a US job seeker unable to review the job opportunities herein, or cannot otherwise complete your expression of interest, without additional assistance and would like to discuss a request for reasonable accommodation, please contact our Global Recruiting organization at GlobalRecruiting@unisys.com or alternatively Toll Free: 888-560-1782 (Prompt 4).  US job seekers can find more information about Unisys’  EEO commitment here.