Machine Learning Ops Lead, Qantev

• +5 years of experience in MLOps, DevOps or software engineering, with focus on ML/AI systems
• Strong experience with cloud platforms (AWS, Azure, GCP) and their ML services
• Proven experience in deploying and managing ML models in production
• Strong programming skills in Python, Linux (Bash) and proficiency with ML frameworks like PyTorch, HuggingFace, ONNX, etc
• Strong knowledge of containerization (Docker) and orchestration tools (Kubernetes)
• Experience with CI/CD pipelines, monitoring tools, and version control (Git)
• Familiarity with data pipeline tools (Airflow, Apache Kafka, Dagster) and model monitoring frameworks
• Expertise in managing and optimizing cloud-based resources for ML workloads
• Strong communication and presentation skills, with the ability to convey complex concepts to non-technical audiences
• Experience in developing APIs
• Experience with ML versioning tooling, including data versioning and model registries
• Fluency in English. Any additional language is a plus

Desirable

• Set the guidelines and standards of MLOps at the company
• Design and manage ML pipelines, from data ingestion and model training to deployment and monitoring
• Ensure safe, stable and performant deep learning model deployment in both real-time and batch flows, considering latency, reliability and scalability
• Implement best practices for version control, CI/CD, and model reproducibility for the ML/DL models
• Develop and maintain infrastructure for automated model training and retraining
• Monitor model performance and implement alerting mechanisms to identify issues such as data-drift and others
• Collaborate with data scientists and software engineers to optimize ML workflows
• Manage cloud infrastructure and resources to support ML workloads efficiently

• Interview with the Talent Acquisition Leader
• Tech Interview 1: Machine/Deep Learning
• Tech Interview 2: Infra/Software/Devops/MLOps
• Interview with the VP of Engineering