AI Platform Engineer

An elite international private investment firm and hedge fund, recognized as one of the largest and most successful financial institutions globally.

The enterprise builds high-performance automated trading infrastructures, real-time pricing systems, risk management engines, and live market telemetry platforms.

Operating an extensive engineering ecosystem worldwide, the local development and innovation hub in Israel designs and engineers sophisticated, large-scale financial platforms and AI-driven systems from scratch.

The company is located in Ramat Gan (train-accessible) and operates on a fully on-site work model.

Key Responsibilities-

• Constructing and maintaining end-to-end, high-volume ETL/ELT pipelines supporting both high-throughput batch and real-time data processing.
• Writing clean, production-grade automation scripts, backend workflows, and infrastructure logic primarily utilizing Python.
• Designing and executing advanced, optimized database schemas and complex SQL queries, tuning performance for low-latency analytics.
• Provisioning and managing secure cluster environments, microservices orchestration, and resource schedulers utilizing Kubernetes.
• Integrating intelligent AI-assisted tooling and automation modules into internal data operations to streamline debugging, cluster tracking, and performance tuning.

Requirements-

• 3 years of professional software engineering experience operating as a Data Engineer, MLOps Specialist, or Infrastructure Engineer (Mandatory).
• Strong, production-grade programming and scripting proficiency utilizing Python alongside expert-level SQL capabilities (Mandatory).
• Comprehensive hands-on experience designing and operating data pipelines within Google Cloud Platform (GCP) environments (Mandatory).
• Extensive experience engineering high-volume, performance-tuned ETL/ELT processes across both batch and real-time streaming architectures (Mandatory).
• Practical technical familiarity or direct exposure building scalable containerized environments utilizing Kubernetes (Mandatory).
• Demonstrated experience utilizing or developing AI-assisted tooling, automation frameworks, or managing complex ML data components (such as RAG workflows or model monitoring systems).
• Exceptional analytical and problem-solving skills, with a proactive approach to managing multiple technical tasks independently from inception to production deployment.