Senior Staff Software Engineer, AI

Overview

Come join the Intuit team as a Senior Staff AI Engineer.

Our Data Exchange group is responsible for acquiring millions of transactions and statements a day to satisfy our customer’s needs in all Intuit products.

You will utilize your skills to help develop and maintain backend services leveraging AI and machine learning models, using both analytical algorithms and deep learning approaches, to acquire data from financial institutions on behalf of our users.

Responsibilities

• Lead and apply best practices in AI driven software lifecycle management, from ideation, through development and evaluation to production deployment.
• Build a backend service with AI in its core at scale (millions of users and requests daily)
• Collaborate with stakeholders to define success criteria and align model metrics with business goals
• Work side-by-side with product managers, business analytics, data scientists, and backend engineers in enabling AI solutions for business use cases
• Explore the state-of-the-art technologies and apply them to deliver customer benefits
  
  

• 15+ years industry experience
• 5+ years industry experience bringing AI models from modeling to production
• Expertise and experience in data mining algorithms and statistical modeling techniques such as classification, regression, clustering, anomaly detection, and text mining
• Strong understanding of the Software design and architecture process
• Experienced with working in cloud production-grade high-scale microservices environment
• Languages such as Python & Java
• Building and maintaining AI based applications at scale in production
• Experience with agentic systems or multi-agent orchestration in AI workflows and AI observability practices.
• Exposure to Knowledge Graphs, RAG (Retrieval-Augmented Generation), or semantic search.
• Understanding of AI infrastructure components, including the prompt lifecycle, fallback logic, and feature-level configuration.
• Excellent oral and written English communication skills: demonstrated ability to explain complex technical issues to both technical and non-technical audiences
• BS, MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research), or equivalent work experience
  
  

• Data science model training:
• Well versed in Data Science languages, tools and frameworks, including data processing platforms and distributed computing systems (for example Python, R, SQL, SKLearn, NumPy, Pandas, TensorFlow, Keras)
• Familiarity with vector database
• Machine Learning experience