RESPONSIBILITIES

As the ML Ops lead at Qantev, you will get an opportunity to:

• 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

REQUIREMENTS

• +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

Bonus skills:

• Experience in the health insurance industry

• Experience with setting up and managing GPUs for Accelerated Deep Learning

• Strong background on Deep Learning

• Talent Acquisition interview

• Tech Interview 1: Machine/Deep Learning

• Tech Interview 2: Infra/Software/Devops/MLOps

• On-Site Interview with the CTO