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 

• End-to-End Model Deployment: Collaborate closely with data scientists and ML engineers to take over models and deploy them into production environments. 

• Future infrastructure design: Bridge with the IT department to define requirements for the present and future infrastructure. 

• Pipeline Automation: Develop and maintain CI/CD pipelines tailored for ML workflows, automating model versioning, testing, and deployment. 

• Monitoring and Maintenance: Implement monitoring systems to track model performance, data drift, and system health, ensuring proactive maintenance and scalability. 

• Standardization and Best Practices: Establish ML Ops best practices, setting the foundation for future growth and scalability. 

• Educational Background: Master’s degree in Computer Science, Engineering or a related quantitative field. 

• Experience: 

• 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. 

• Technical Skills: 

• 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. 

• Soft Skills: 

• 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.