Machine Learning Engineer

We are Make IT Real Tech!

An information technology firm that provides consulting services in IT, software, and advanced areas such as Artificial Intelligence. Our global team of experts is committed to creating AI-driven workflows and solutions that enhance efficiency across the manufacturing, supply chain management, and e-commerce space. By leveraging latest technologies, we offer customized IT solutions that place us at the forefront of innovation, driving advancements in both customer engagement and operational efficiency.

We have a brand-new opportunity for a Machine Learning Engineer.

In this role, you will work on the full ML lifecycle, from data pipeline development to model deployment and monitoring in production environments. Sounds good? Then keep reading!

Why You Will Love Working With Us

• Global Collaboration: Gain international experience by working with globally distributed teams
• Flexible Work Options: Enjoy remote or hybrid work arrangements that suit your lifestyle
• Work-Life Balance: Flexible working hours help you balance your professional and personal life
• Private Health Insurance: Comprehensive coverage for your peace of mind
• Extra Leave: Additional paid leave for special occasions
• Growth Opportunities: Access to valuable knowledge and experience to support your career development
• Team Building: Connect with colleagues through team-building activities and company events
• Innovation and AI: Be part of an AI-first workplace that enables everyone to drive unique business solutions through state-of-the-art technology
  
  

• Design, develop, and implement machine learning models for various business applications including recommendation systems, classification, and prediction tasks
• Conduct experiments to evaluate different modeling approaches and select optimal solutions based on performance metrics and business requirements
• Transform proof-of-concept models into production-ready systems with appropriate error handling and scalability
  
  

• Build robust data pipelines for feature engineering, model training, and inference
• Implement data quality checks and monitoring systems to ensure reliable model inputs
• Optimize data processing workflows for efficiency and cost-effectiveness
  
  

• Deploy models to production using containerization and orchestration tools
• Implement model versioning, A/B testing frameworks, and rollback capabilities
• Design and maintain model monitoring systems to track performance, detect drift, and trigger retraining
• Collaborate with platform teams to ensure models meet latency, throughput, and reliability requirements
  
  

• Partner with product managers and business stakeholders to understand requirements and translate them into ML solutions
• Work with data ETL engineers and BI analysts to ensure proper data flow and model integration
• Collaborate with software engineers to integrate ML systems into existing applications
• Document technical designs, model architectures, and deployment procedures
  
  

• Bachelors degree in computer science, Engineering, Mathematics, or related technical field, or equivalent practical experience
• 3+ years of experience building and deploying machine learning systems in production environments
• Demonstrated experience with the complete ML project lifecycle from problem formulation to production deployment
  
  

• Strong programming skills in Python and proficiency with ML frameworks (TensorFlow, PyTorch, or JAX)
• Experience with AWS cloud platform and its ML services (SageMaker, Lambda, EC2, S3)
• Experience with MLOps tools and practices including experiment tracking, model registries, and CI/CD for ML
• Strong understanding of software engineering principles including version control, testing, and code review practices
• Experience with containerization (Docker) and familiarity with orchestration concepts
  
  

• Solid understanding of ML fundamentals including supervised and unsupervised learning, feature engineering, and model evaluation
• Experience with deep learning architectures and their practical applications
• Knowledge of model optimization techniques including quantization, pruning, and distillation
• Understanding of common production ML challenges such as data drift, model degradation, and online learning