MLOps Engineer (f/m/div.)

Requirements

• 2+ years of experience in MLOps, Data Engineering, DevOps, or similar roles.
• Technical Skills :
• Good Understanding of the ML development process and model life cycle;
• Good understanding of DevOps principles and practices;
• Experience designing, implementing, integrating, and maintaining MLOps solutions or CI/CD pipelines;
• Good scripting and automation skills with Python and Bash;
• Knowledge of Cloud solutions , preferably Azure;
• Basic understanding of Terraform, GitHub Actions, and Kubernetes;
• Experience with GNU/Linux systems as key development environment.
• Soft Skills :
• Excellent problem-solving skills;
• Effective communication skills, team player, and capable of collaborating across functional areas;
• Proactive, customer and result-oriented personality;
• Fluent in English, written and spoken;
• Experience working in Agile/SAFe environments.
• Work #LikeABosch includes:
• ⚖️ Flexible work conditions
• 🔀 Hybrid work system
• 🌐 Exchange with colleagues around the world
• 🧑⚕️ Health insurance and medical office on site (psychology and general clinic)
• 📚 Training opportunities (p.e., technical training, foreign languages training) & certifications
• 📈 Opportunities for career progression and continuous professional development
• 💲 Access to great discounts in partnerships and Bosch products
• 🏋️ Sports and health related activities
• 🚉 Great access to public transports
• 🚌 Free transport from Porto
• 💰 Flexible benefits platform
• 🅿️ Free parking lot
• 🍽️ Canteen
• Success stories don´t just happen. They are made...
• Make it happen! We are looking forward to your application!

Benefits

Additional Information

MLOps Strategy & Architecture Contribute to the definition and evolution of the MLOps strategy for a hybrid cloud environment, ensuring alignment with business objectives, security standards, and industry best practices; Support the design of a scalable MLOps platform with a self-service approach, covering the full ML lifecycle, including data ingestion, feature engineering, model training, validation, deployment, and monitoring; Develop and maintain clear and structured documentation for MLOps processes, workflows, and infrastructure. Hybrid ML Lifecycle Implementation Design and implement Infrastructure as Code (IaC) solutions using Terraform to provision and manage cloud and on-premises resources; Ensure robust security practices to protect sensitive data and ML models across hybrid environments; Develop and deploy new platform functionalities while ensuring key non-functional requirements, particularly reproducibility and reliability; Build and maintain monitoring dashboards and alerting systems to proactively detect and resolve platform issues. Collaboration & Contribution Collaborate closely with ML engineers, data scientists, software engineers, and infrastructure teams to deliver a scalable and high-quality MLOps platform; Communicate effectively with both technical and non-technical stakeholders, ensuring alignment and transparency; Stay up to date with emerging MLOps trends, tools, and technologies, actively contributing to continuous improvement; Participate in code reviews and contribute to the definition and adoption of best practices. Education : MSc or Ph.D. in Software Engineering, Computer Science, Data Science, Machine Learning or a related field.

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