The Intuit T4I (Tech for Intuit) team is responsible for building Intuit’s core capabilities supporting enterprise Workforce, People, and Places technologies. Our stakeholders include HR, Facilities, IT, segment business unit leaders, and all Intuit employees and expert workforce. The T4I engineering organization partners closely with product, design, and platform teams to deliver primarily internal services that improve productivity and employee experience across Intuit.
As a Staff ( Software | ML) Engineer, you will play a critical role in designing, building, and scaling AI-powered agents and copilots that support Intuit’s workforce. This role is focused on applied AI systems, not research—delivering production-ready agent architectures that integrate with enterprise systems and workflows.
You will help define how and where AI agents add real value, making thoughtful tradeoffs between LLM-based approaches, traditional ML techniques, and simpler heuristic or rules-based solutions. You will work across backend services, AI platforms, and conversational interfaces to enable intelligent automation at scale.
This role is well suited for senior backend engineers with strong AI systems experience or ML engineers with strong software engineering foundations who are excited about building end-to-end agentic systems.
If you have a bias for action, enjoy operating in ambiguity, and are motivated by building practical AI solutions that drive measurable business impact, this role may be a great fit.
Responsibilities
Responsibilities
Design and Build AI Agents: Architect, develop, and deploy AI agents and copilots that augment Intuit employees’ workflows, integrating with internal systems and tools.
Own End-to-End AI Systems: Take solutions from concept to production—including model selection, prompt and context design, retrieval strategies, backend services, and conversational interfaces.
Apply the Right Tool for the Problem: Evaluate business problems and determine whether they are best solved with LLMs, classical ML, or simpler rule-based approaches.
Develop RAG and Context Pipelines: Build scalable, maintainable retrieval and context systems that provide high-quality grounding for AI agents.
Innovate Through Rapid Prototyping: Create proofs-of-concept to explore new agent capabilities, frameworks, and integrations, then harden successful ideas for production use.
Ensure Production Readiness: Design for reliability, observability, security, and cost efficiency in AI-powered systems operating at enterprise scale.
Collaborate Cross-Functionally: Partner closely with product managers, designers, platform teams, and business stakeholders to deliver meaningful outcomes.
Mentor and Influence: Provide technical leadership, mentor other engineers, and help shape best practices for AI agent development across the organization.
Qualifications
8+ years of experience in software engineering, machine learning engineering, or related roles building large-scale, production systems.
Hands-on experience designing and deploying AI systems using large language models (LLMs) in real-world applications.
Strong understanding of when to apply LLMs vs. traditional ML algorithms vs. deterministic or heuristic approaches, with the ability to justify tradeoffs.
Experience building and maintaining:
-
Retrieval-Augmented Generation (RAG) pipelines
-
Agent frameworks and orchestration patterns
-
Model Context Protocol (MCP) servers or equivalent tool-use / function-calling architectures
-
Conversational or chat-based interfaces (internal tools, copilots, or assistants)
Strong backend engineering skills (e.g., Python, Java), including API design, service ownership, and distributed systems fundamentals.
Familiarity with deploying AI systems on cloud platforms (AWS), including observability, reliability, and cost considerations.
Experience integrating AI systems with enterprise data sources, APIs, and internal platforms while maintaining security and privacy standards.
Ability to work independently, manage ambiguity, and deliver incrementally under aggressive timelines.
Passion for mentoring engineers and influencing technical direction beyond your immediate team.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: $184500 to $266,500
