Intuit Staff Software Engineer AI 

Israel, Center District, Petah Tikva 

181307031

Role Overview

As a Staff AI/ML Engineer on the Data Exchange team, you will:

• Lead and apply best practices in AI/ML Project lifecycle management: scoping, data preparation, deployment, and monitoring
• Collaborate widely: Work cross-functionally with product managers, data scientists, backend engineers, and business stakeholders to deliver intuitive, customer-focused applications.
• Be expected to help architect, integrate, optimize, deploy, and code high scale resilient services

• Design and Build scalable Gen AI solutions: Architect, implement and integrate, and optimize production Backend services and pipelines powered by AI native systems at high scale.
• End-to-End Model Ownership: Manage the entire ML lifecycle—from problem definition and distributed feature engineering, through model development, deployment, and ongoing monitoring.
• Cross-Functional Partnership: Partner with product, analytics, and engineering teams to translate business needs into AI solutions.
• Innovation & Exploration: Stay abreast of and experiment with the latest advancements in AI/ML to bring customer value.
• Ensure high performance , scalability , and resilience of deployed AI-powered services.

• Experience: maintain and build AI/ML models in production at scale.
• Gen AI & LLMs: Proven track record with Gen AI in live systems and large language model–based applications.
• Algorithms & Modeling: Deep expertise in classification, regression, clustering, anomaly detection, and text mining.
• Cloud & Microservices: Hands-on experience with production-grade, high-scale microservices (Kubernetes, Docker, Spring Boot).
• SW Engineering: Proficiency in Python and Java/Kotlin; strong SQL skills; comfortable in Linux environments.
• AI Infrastructure: Understanding of prompt lifecycle, fallback logic, feature-level config, and AI observability.
• Communication: Excellent oral and written English, with the ability to explain complex technical concepts to both technical and non-technical audiences.
• Education: BS, MS, or PhD in Computer Science, Statistics, Applied Math, Operations Research, or equivalent experience.

Preferred Qualifications (Advantage)

• Data Science Tooling: Skilled in R, Pandas, NumPy, scikit-learn, TensorFlow, Keras, and distributed computing platforms.
• Vector Databases & RAG: Experience with retrieval-augmented generation, semantic search, or knowledge graph integration.
• Agentic Systems: Familiarity with multi-agent orchestration in AI workflows.