AI / Machine Learning Software Engineer

Cyclad supports top international IT companies by delivering innovative technologies in the Automotive / IT industry.

• Strong commercial experience in Python development
• Hands-on experience with Machine Learning and building ML-based solutions
• Experience with ML frameworks such as PyTorch, TensorFlow and/or scikit-learn
• Experience deploying and integrating ML models into production applications
• Good understanding of software engineering principles, clean code, testing and application architecture
• Experience developing REST APIs / backend services
• Knowledge of Docker and containerized applications
• Experience with Kubernetes
• Knowledge of MLOps concepts and tools, including model deployment, monitoring and CI/CD
• Experience with at least one major cloud platform (AWS, Azure or GCP)
• Experience working with SQL and data-processing technologies
• Familiarity with Git and CI/CD
• Very good command of English

Nice to have:

• Experience with Spark / PySpark
• Experience with Cloudera
• Knowledge of Apache Iceberg
• Experience with distributed data processing and large-scale datasets
• Experience with Generative AI / LLMs, RAG or AI agents

• Designing and developing AI and Machine Learning solutions for production environments
• Building scalable software components and services integrating ML models
• Developing and maintaining applications and APIs using Python
• Implementing the complete ML lifecycle: data preparation, model development, evaluation, deployment and monitoring
• Productionizing Machine Learning models and integrating them with existing systems
• Developing MLOps pipelines for training, deployment and monitoring
• Working with large datasets and data-processing frameworks
• Containerizing and deploying AI/ML applications using Docker and Kubernetes
• Working with cloud environments and cloud-native services
• Ensuring code quality through testing, code reviews and software engineering best practices
• Monitoring model and application performance in production
• Collaborating with Data Scientists, Data Engineers, Software Engineers and other technical stakeholders