ML Ops engineer

Building the bank of tomorrow takes more than skills.

It means combining our differences to imagine, discuss, code, develop, test, learn… and celebrate every step together. Share our vibes? Join Swissquote to unleash your potential.

• We are the Swiss Leader in Online Banking and we provide trading, investing, and banking services to +500,000 clients through our performant and secured digital platforms.
• Our +1000 employees work in a flexible way, without dress code and in multicultural teams. By having a huge impact on the industry, they are growing their skills portfolio and boosting their career in a fast-paced environment.
• We are all in at Swissquote. As an equal opportunity employer, we welcome candidates from all backgrounds, experiences, and perspectives to join our team and contribute to our shared success.

We are looking for a versatile ML Ops Engineer who is ready to take on the exciting challenge of establishing ML Ops practices within our organization. As the first dedicated ML Ops Engineer in our team, you will play a crucial role in taking over models from data scientists and deploying them to robust and scalable production environments. You will ensure that our models not only perform well but are also maintained efficiently, enabling continuous improvement and business impact.

Key Responsibilities:

• Collaborate closely with data scientists and ML engineers to take over models and deploy them into production environments.
• Bridge with the IT department to define requirements for the present and future infrastructure.
• Develop and maintain CI/CD pipelines tailored for ML workflows, automating model versioning, testing, and deployment.
• Implement monitoring systems to track model performance, data drift, and system health, ensuring proactive maintenance and scalability.
• Establish ML Ops best practices, setting the foundation for future growth and scalability.

Qualifications:

• Master’s degree in Computer Science, Engineering, or a related quantitative field.
• Proven experience of at least 2 years as an ML Ops Engineer or in a similar role, ideally in dynamic and growing teams.
• Experience working closely with data scientists to transition models and genAI applications from development to production.
• Strong programming skills in Python, with knowledge of Java being a plus.
• Proficiency in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with generative AI models, including leveraging proprietary APIs and deploying on-site RAG systems.
• Strong experience with cloud platforms.
• Proficiency in CI/CD tools (GitLab CI or similar).
• Expertise in containerization and orchestration (Docker, Kubernetes).
• Experience with data pipeline orchestration tools (e.g., Airflow) is a plus.
• Familiarity with data streaming and monitoring tools like Kafka and Elasticsearch is a plus.
• High degree of autonomy and proactive behavior.
• Versatility and adaptability to take on new challenges in a fast-growing environment.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
• Problem-solving mindset with a proactive and self-starter attitude.