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Intuit Staff Software Engineer AI 
Israel, Center District, Petah Tikva 
181307031

Today

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
Responsibilities
  • 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.
Qualifications
  • 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.