Senior MLops Engineer

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.