aiOla is seeking an MLOps Engineer to join our team. This role will play a significant pillar within all R&D departments, having a tremendous impact on our success. This is a fantastic opportunity to join aiOla at the scaling stage and play a significant role in our growth and success. We are a rapidly growing, product-led, SaaS startup dedicated to revolutionizing efficiency, intelligence, collaboration, and safety through our proprietary speech-powered AI technology. With strong investment from top-tier VCs including New Era Capital Partners and Hamilton Lane, we are well-positioned to make a significant impact in the industry.

At aiOla, we have a talented team of professionals spanning across Israel and the US, encompassing Product, Data Science, Data Engineering, Analytics, Marketing, and Customer Success. Our culture thrives on collaboration, innovation, and a shared ambition to excel. We are looking for visionary thinkers and innovative creators who are passionate about pushing the boundaries of AI and making a tangible impact in everything we do. Join us on an incredible journey of continuous learning and personal growth.

Requirements:

• 3+ years of hands-on experience as an MLOps or ML Engineer.
• Proven track record in building and managing ML pipelines, and CI/CD processes and tools.
• Extensive experience in ML workflows and Data Orchestration frameworks such as AirFlow, Prefect, MLFlow, Kubeflow, SageMaker, etc.
• Familiarity with container orchestration tools, including Kubernetes.
• Experience with AWS cloud-based services.
• Ability to write efficient, scalable Python code.
• Experience with source control (e.g., Bitbucket, Git).
• B.Sc. in Computer Science, Engineering, Math, or another quantitative field - an advantage.
• Strong problem-solving skills with good analysis for root cause detection.
• Ability to work both collaboratively with a team and independently.
• Self-learner with a can-do attitude.
• Working Hybrid- 3 days a week from the office**
  
  

Responsibilities:

• Build the infrastructure for the ML lifecycle, from development to deployment and monitoring.
• Work together with Data Scientists, Data Engineers, Software Engineers, and Product teams to train, deploy, and manage ML models throughout their lifecycle - from development to production.
• Design, implement, manage, monitor, and optimize a scalable and robust infrastructure for machine learning workflows.
• Implement metrics-based processes to improve the accuracy and reliability of our ML models, including early detection and mitigation of performance issues.
• Implement and manage CI/CD pipelines for machine learning workflows.
• Automate model training, retraining, testing, validating, and deployment processes.
• Proactively identify and resolve issues related to model performance and data quality.
• Communicate effectively with stakeholders to understand requirements and provide updates on model deployment and performance.