Machine Learning Engineer (MLE)

We are looking for an experienced Machine Learning Engineer (MLE) to join the Lab Unit at Central Israel. The Lab Unit is at the forefront of technology innovation, tasked with building data-driven solutions and advanced models to support strategic goals.
In this role, you will be responsible for all production aspects of Machine Learning models. The work focuses on building, deploying, maintaining, and optimizing processes and pipelines in a production environment, while collaborating closely with the team's Data Scientists. This is inherently an engineering role, designed for someone who lives and breathes MLOps, loves writing high-quality code, and takes ownership of turning complex models into stable, efficient, and scalable systems.
Responsibilities

Transition ML models from development (Research) to production (Production) environments
Build and maintain automated CI/CD pipelines for model training, testing, and deployment
Develop and implement MLOps tools for monitoring model performance, detecting model drift, and setting up alerts
Optimize model performance in production (Latency, Throughput)
Form a full partnership with Data Scientists throughout the entire model lifecycle—from requirements and data analysis, through translating models into viable engineering solutions, to monitoring performance in production
Conduct data analysis and ad-hoc investigations to troubleshoot production issues, understand model drift, and validate new data sources

Requirements

Academic degree in a relevant technological field
1–2 years of experience in a DevOps or ML Engineering role
Proven experience transitioning models from research environments (research/notebooks) to production environments
Deep and high-level proficiency in Python, including OOP and software engineering principles
Experience with MLOps – a Must (Significant advantage for experience with Dataiku)
Proficiency in a Linux environment and writing shell scripts
Hands-on experience with Docker and a good understanding of Kubernetes/OpenShift
Experience with CI/CD processes (e.g., Jenkins, Git/Bitbucket)
Strong command of SQL, working with databases, and building ETL processes
Experience with monitoring and logging tools (e.g., Splunk, Prometheus, Grafana)
Experience developing and using APIs (e.g., FastAPI, Flask, Django) – Advantage
Hands-on experience implementing and operating GenAI / LLM-based systems – Significant advantage
Practical experience with ML libraries (such as PyTorch, Pandas, NumPy, Scikit-learn)
Ability to work independently and manage tasks end-to-end
A passion for high-quality software engineering, clean code, and automated processes
Ability to bridge the gap between the research world (Data Science) and the operational world (Production)
Strong communication skills and excellent teamwork capabilities, working alongside engineering, data, and research teams