ML Engineer

ITRex is an AI-focused company with over 250 employees across the US and Europe, creating solutions for clients such as Procter & Gamble and Shutterstock. They build practical AI systems that work in real-world applications.

Technical Skills:

• 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
• Strong understanding of machine learning techniques, including supervised & unsupervised learning, NLP, deep learning fundamentals, and model evaluation
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy
• Hands-on experience in containerizing ML applications using Docker for scalable deployment
• Practical experience with at least one cloud provider (AWS, GCP)
• Strong background in working with large datasets, SQL/NoSQL databases
• Ability to decompose complex problems into well-structured ML tasks
• Skilled at assessing whether ML is the best approach or if a simpler solution (e.g., heuristic rules, statistical methods) would be more effective
• Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability
• Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency

Business & Collaboration:

• Excellent communication skills, capable of explaining ML concepts to both technical peers and non-technical stakeholders
• Collaborative, product-focused approach within Agile, cross-functional environments
• Proactive mindset with a strong sense of ownership with the ability to lead ML tasks end-to-end, from discovery and experimentation to production deployment and support
• Experience working closely with Product, Engineering, Data, DevOps, and business teams to align technical solutions with business goals
• Continuous learning mindset with awareness of current ML/AI trends, tools, and best practices
• English proficiency at an Upper-Intermediate level or above

Nice to have:

• Understanding the business impact of ML models and how to align them with organizational goals
• Experience with feature stores, model registries, and ML model lifecycle management
• Experience designing and developing Retrieval-Augmented Generation (RAG) solutions
• Hands-on experience with AI tools in ML workflows

• Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks
• Implement data pipelines for ingestion, preprocessing, feature engineering, and model training
• Containerize ML models and applications using Docker for scalable and reproducible deployments
• Deploy and maintain ML solutions in cloud environments (AWS/Snowflake)
• Optimize model performance, latency, and resource utilization for real-time or batch inference
• Monitor and troubleshoot ML models in production, ensuring reliability and robustness
• Collaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems
• Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness
• Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate