(Senior) ML Ops Engineer at Continental

Vila Nova de Famalicão, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

15 Sep, 26

Salary

0.0

Posted On

17 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, CI/CD, GitHub Actions, Docker, Kubernetes, MLflow, SageMaker, AWS, Azure, Infrastructure-as-Code, Grafana, Airflow, GitOps, Scrum, Kanban

Industry

Motor Vehicle Manufacturing

Description

Company Description Continental is a leading tire manufacturer and industry specialist. Founded in 1871, the company generated sales of €39.7 billion in 2024 and currently employs around 95,000 people in 54 countries and markets. Tire solutions from the Tires group sector make mobility safer, smarter, and more sustainable. Its premium portfolio encompasses car, truck, bus, two-wheel, and specialty tires as well as smart solutions and services for fleets and tire retailers. Continental has been delivering top performance for more than 150 years and is one of the world’s largest tire manufacturers. In fiscal 2024, the Tires group sector generated sales of 13.9 billion euros. Continental's tire division employs more than 57,000 people worldwide and has 20 production and 16 development sites. Job Description Join Continental’s Digital Solutions Department as a MLOps Engineer In Digital Solutions, we drive innovation for connected mobility and deliver cutting-edge digital products to our global fleet customers. As a MLOps Engineer, you will be at the forefront of transforming machine learning into scalable, production-ready solutions. You will design, deploy, and maintain robust ML pipelines, ensuring seamless integration of models into production environments and optimizing their performance at scale. Working closely with Data Scientists, Data Engineers, and cross-functional teams, you will enable AI-driven insights that power smarter, safer, and more efficient mobility worldwide. Main responsibilities: Design and manage CI/CD pipelines for ML models from development to production; Build and maintain scalable ML infrastructure on cloud platforms using infrastructure-as-code; Automate deployment, monitoring, and rollback processes for reliability and reproducibility; Implement monitoring and feedback loops for model performance and continuous improvement; Collaborate with data scientists and engineers to integrate ML models and standardize workflows; Provide technical leadership and mentorship, fostering knowledge sharing and best practices. Qualifications Academic degree in Computer Science, IT, Engineering, Mathematics, or related field; >3 years of professional experience as a MLOps Engineer; Strong programming skills in Python and SQL; Expertise in CI/CD and automation tools (e.g., GitHub Actions); Proficiency in containerization and orchestration (Docker, Kubernetes); Hands-on experience with ML model deployment and lifecycle management (MLflow, SageMaker); Good knowledge of cloud platforms (AWS or Azure) and infrastructure-as-code practices; Familiarity with monitoring and performance tracking tools (Grafana) and workflow orchestration (Airflow); Solid understanding of software engineering and DevOps practices (testing, GitOps, CI/CD design patterns); Experience with Agile project management methods (Scrum, Kanban); Experience working in an international team (desirable); Proficient English language skills (spoken / written). Additional Information Our offer: Integration in a challenging and international work environment; Flexible working model; Agile and collaborative working style; Continuous opportunities for personal development and learning. We are committed to fostering a workplace where everyone feels safe, respected, and valued. All kind of applications are welcome. Ready to drive with Continental? Take the first step and fill in the online application. Leadership Level: Leading Self Legal Entity: Continental Solution Center Portugal, Unipessoal, LDA. (0581) Referral Bonus: Yes Working Time: Full Time

Responsibilities

Design and manage CI/CD pipelines and scalable ML infrastructure to transform machine learning models into production-ready solutions. Collaborate with cross-functional teams to automate deployment, monitoring, and performance tracking for AI-driven mobility insights.