About NAYA:

We are a leading global provider of data platform and development professional services.

We are proud to be one of the fastest-growing data and development technology companies worldwide, based in Israel — and we're hiring.

We are looking for an experienced and results-oriented MLOps / ML Engineer to join our data team. In this role, you will act as the critical bridge between Data Science (Research) and Software Engineering (Production). You will be responsible for engineering, deploying, optimizing, and monitoring complex models at scale, implementing cutting-edge workflows and automation tools.

Key Responsibilities:

• Model Productionization: Transition complex ML models from development and research environments (Research/Notebooks) into stable, scalable, and secure production environments.
• CI/CD for ML: Build, maintain, and automate pipelines for model retraining, testing, and deployment.
• Monitoring & Alerts: Develop and implement MLOps tools to monitor model performance in real-time, detect model and data drift, manage alert systems, and optimize model performance (Latency & Throughput).
• Cross-Functional Collaboration: Partner closely with Data Scientists throughout the entire model lifecycle—from initial requirements and data analysis to translating algorithms into sustainable engineering solutions and monitoring them in production.
• Data Investigation: Conduct ad-hoc data analysis and deep-dive investigations to troubleshoot production issues, understand model drift, and validate new data sources.

Requirements:

Must-Have Qualifications:

• Education: Academic degree in a relevant technological field (Computer Science, Software Engineering, Information Systems, or a quantitative discipline).
• Experience: 1–2 years of hands-on experience in a DevOps, ML Engineering, or Backend Engineering role with a strong affinity for data.
• Software Engineering: Deep and advanced knowledge of Python (including OOP, clean code principles, and design patterns).
• Model Deployment: Proven, hands-on experience transitioning models from research/notebooks to production environments.
• Microservices & Infrastructure: Practical experience with Docker and a solid understanding of Kubernetes / OpenShift.
• CI/CD & OS: Strong proficiency in Linux environments, shell scripting, and CI/CD tools (e.g., Jenkins, Git/Bitbucket).
• Data Management: High proficiency in SQL, experience working with relational/non-relational databases, and building ETL processes.
• ML Libraries: Hands-on experience with core ML and data libraries (e.g., PyTorch, Pandas, NumPy, Scikit-Learn).

Nice-to-Have (Advantages):

• MLOps Platforms: Direct experience with Dataiku – Highly Significant Advantage.
• Generative AI: Practical experience deploying and operating systems based on GenAI / LLMs in production – Significant Advantage.
• API Development: Experience developing and exposing APIs (e.g., FastAPI, Flask, Django).
• Observability: Experience with monitoring, logging, and visualization tools (e.g., Splunk, Prometheus, Grafana).