Principal Machine Learning Engineer (AI)

Location: Tel Aviv, Israel — Hybrid

Company

hirify.global develops cloud identity and access management technology for secure digital experiences and serves large enterprises worldwide.

What you will do

• Architect, build, and operate scalable ML platforms, tools, and services for model promotion, evaluation, deployment, and monitoring.
• Design feature stores with offline/online consistency, feature reuse, lineage, and governance across training and serving.
• Build real-time streaming feature pipelines and low-latency, sub-second model serving for critical-path identity decisions.
• Develop automated data pipelines with Python, SQL, Airflow, and Spark, and manage distributed platforms using Spark, Kafka, and cloud-native services.
• Drive end-to-end MLOps with CI/CD, Docker, Kubernetes, monitoring, and continuous model lifecycle management.
• Shape architecture and technical direction, create utilities for data scientists, collaborate with Data Science, SRE, and Product teams, and mentor engineers.

Requirements

• 6+ years of experience as an ML Engineer building ML platforms, data infrastructure, and large-scale distributed systems.
• Expert-level Python and SQL programming skills.
• Deep hands-on experience with Spark or similar distributed computing frameworks.
• Production experience with Airflow, MLflow, Kubernetes, and CI/CD pipelines.
• Hands-on experience with offline and online feature stores, real-time ML streaming data using technologies such as Kafka and Flink, and low-latency model serving.
• Experience with public cloud platforms, preferably AWS, including managed data and ML services such as AWS SageMaker.
• Experience developing monitoring tools and utilities for data scientists and improving model training and deployment processes.
• Ability to combine broad architectural thinking with efficient low-level implementation.
• Strong interpersonal communication, collaboration, problem-solving skills, and effectiveness in fast-paced agile environments.

Nice to have

• Experience with modern lakehouse and data platform technologies such as Apache Iceberg, dbt, OpenMetadata, and data governance or lineage.
• Experience operating ML infrastructure across multi-account cloud environments with a self-service, low-ops approach.
• Familiarity with deep learning, LLMs, reinforcement learning, or PyTorch.
• M.Sc. in an applied science field.
• Java experience.

Culture & Benefits

• Flexible, collaborative work environment with a culture of knowledge sharing and shared success.
• Employee resource groups, team bonding events, volunteering initiatives, and an inclusive environment.
• Generous PTO and holiday schedule, parental leave, healthcare options, and retirement programs.
• Education reimbursement and commuter offset for specific locations.