RESPONSIBILITIES
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
HIRING PROCESS
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
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