Uni Systems

Infrastructure Engineer

• Design, implement and maintain a scalable, reliable and secure hybrid cloud ML Ops infrastructure to deploy, test, manage and monitor ML models in different environments;
• Development and maintenance of software applications in the field of Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL) and/or Artificial Intelligence (AI);
• Work closely with data scientists and back-end developers to build, test, integrate and deploy ML models;
• Analyse performance metrics and troubleshoot issues to ensure high availability and reliability;
• Design CI/CD pipelines, use orchestration solutions and data versioning tools;
• Creating automated anomaly detection systems and constant tracking of its performance and optimising ML pipelines for scalability, efficiency and cost-effectiveness.;
• Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts;
• Provision of security studies, security assessments or other security matters associated with information system projects;
• Provision of support and guidance to other team members on MLOps practices.

Machine Learning Infrastructure Engineer

Business Analyst

• Collect business requirements and analyze advanced AI solutions or identify and assess relevant existing data mining, machine learning and business intelligence solutions.
• Develop detailed application mock-ups using design tools, ensuring alignment with business objectives and user needs.
• Analyze large datasets to uncover actionable insights.
• Ensure strict compliance with GDPR and emerging AI regulations in all stages of data processing.
• Facilitate effective communication and collaboration between business stakeholders, data scientists, and IT developers.
• Conduct user acceptance testing to meet business requirements.
• Specification and design of presentation interfaces with optimal usability/user experience.
• Produce data models according to specific problems statements.
• Contribute to the design and implementation of the analytics architecture and its solution stack (including performance aspects, physical design, capacity dimensions etc.).
• Write the different documentation associated with the tasks and liaise with other project teams as necessary to address cross-project interdependencies.
• Interact with data stewards and other IT stakeholders to define the data rules.
• Define data controls and implement actions to ensure data quality and integrity.
• Create automated anomaly detection systems and constant tracking of its performance.
• Data mining using state-of-the-art methods.
• Processing, cleansing, and verifying the integrity of data used for analysis.
• Participates in the design of the IT architecture for solutions in the NLP / ML / AI fields and coordinates its implementation considering master- and meta-data management concepts.
• Analyze data architecture for consistency, completeness, accuracy and reasonableness.
• Contributing for the analysis of data management vision, strategy and policy and derive the IT requirements.
• Analysis of Business requirements.

AI Business Analyst

Backend Engineer

• Development of front-end website
• Development of back-end website
• Design and develop APIs
• Building reusable code for future use
• Development and integration of technological components
• Implementation of user requirements
• Prototyping
• Elaboration of test programs
• Integration with other applications
• Writing of technical documentation
• Assistance with deployment and configuration of the system
• Participation in meetings with the project teams

Machine Learning Infrastructure Engineer

AI Business Analyst

FullStack Java Developer