ML Engineer

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT. Z nami znajdziesz konkurencyjne oferty zatrudnienia oraz ciekawe projekty IT skierowane zarówno do ekspertów z wieloletnim doświadczeniem, jak i osób, które dopiero zaczynają swoją przygodę w branży IT.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• Proven experience (4+ years) in deploying machine learning models in production environments.
• Strong understanding of machine learning, deep learning, NLP, and generative AI techniques.
• Proficiency with MLOps tools and frameworks such as MLflow, Kubeflow, TensorFlow Extended (TFX), or similar.
• Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
• Proficiency in programming languages such as Python and familiarity with ML/DL frameworks like TensorFlow, PyTorch, and scikit-learn.
• Experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services.
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Strong understanding of version control systems (e.g., Git) and collaborative development workflows.
• Excellent problem-solving skills and the ability to design robust, scalable MLOps solutions.
• Strong communication skills, with the ability to collaborate effectively with cross-functional teams.

• Design, implement, and maintain end-to-end MLOps pipelines for deploying machine learning models into production.
• Collaborate with data scientists to understand model requirements and ensure smooth deployment and integration.
• Develop and manage infrastructure for model training, validation, deployment, monitoring, and retraining.
• Implement CI/CD pipelines to streamline the deployment and updates of AI/ML models.
• Ensure the scalability, reliability, and performance of deployed models through continuous monitoring and optimization.
• Utilize containerization and orchestration tools (e.g., Docker, Kubernetes) to manage model deployment environments.
• Work with cloud platforms (AWS, GCP, Azure) to deploy and manage AI/ML services.
• Implement security best practices for AI/ML models and data pipelines.
• Troubleshoot and resolve issues related to model deployment and operation.
• Stay updated with the latest MLOps tools, frameworks, and methodologies.
• Document processes, configurations, and procedures to ensure knowledge sharing and continuity.

• 100% remote
• Multisport card
• Private healthcare
• Life insurance