Tietoevry Create Ukraine (formerly Infopulse Ukraine) is seeking a Senior Machine Learning Developer to design and implement intelligent personalization systems for workplace automation and user experience enhancement. You will participate in ML architecture design, performance optimization, and end-to-end MLOps automation with a focus on contextual recommendations, experience personalization and behavioral pattern recognition.Technical Core RequirementsML Engineering Expertise· Training Pipeline Design: Best practices for efficient model training, bottleneck detection, and resource utilization· Personalization Systems: Context-aware recommendations, behavioral analytics· MLOps Automation: End-to-end Azure ML pipelines(Kubernetes orchestration nice to have) and CI/CD integration· Performance Optimization: Production-ready model serving, real-time inference, and scalable architecture design· Framework Proficiency: PyTorch/XGBoost/LightGbm (or similar) production implementation with performance optimizationCore Competencies· ML Training Pipeline Excellence: Design efficient training workflows, identify bottlenecks, optimize resource utilization· User-Centric Model Design: Incorporate user feedback, behavioral patterns, and business requirements into ML solutions· Production-Ready Development: Build scalable, maintainable ML systems with proper testing and monitoring· Cross-Functional Collaboration: Work effectively with product teams, stakeholders, and end-users to deliver impactful solutionsRequired Experience· 5+ years of experience building end-to-end ML solutions and custom models· Proven track record building real-time, context-aware ML systemsKey Responsibilities· Solution ownership with strong focus on business needs· Design and implement personalization algorithms for workplace automation· Create behavioral analytics for user pattern recognition and prediction· Establish Azure ML pipelines· Build automated testing, monitoring, and CI/CD for ML models· Optimize ML pipelines for performance, scalability, and cost-effectiveness· Code reviewsTeam Leadership· Conduct technical reviews and establish ML engineering best practices· Mentor team members on performance optimization and model design· Collaborate with product teams to translate requirements into ML solutions· Incorporate user feedback and business metrics into model iterationsProject Focus AreasIntelligent Workplace AutomationBuild ML systems for office exploration, personalization, and visual booking experiences:· Office Intelligence: Automated information extraction, contextual recommendations, and navigation assistance· Behavioral Personalization: Pattern recognition, proactive suggestions, and context-aware recommendations· Experience & Qualifications: Intelligent assistance creation