AI Technical Lead

Crowe · Austin, TX · Yesterday

Engineering$102k–$204k/yrFull-time

About Crowe AI

Everything we do is about making the future of human work more purposeful. We do this by leveraging state-of-the-art machine learning, generative AI, modern architectures, and industry experts to create AI-powered solutions that transform the way we do business. Our world-class AI and ML professionals give Crowe the ability to capture and harness the power of data. A combination of AI and Machine Learning Engineers and AI Platform and Infrastructure Engineers create and deploy AI functionality to solve complex problems. We focus on building scalable AI solutions, based on rigorous data exploration, validation via proof-of-concept buildout, and close partnership with subject matter experts across business areas.

Role Overview

Crowe's Studio team is seeking an AI Tech Lead to serve as a hands-on technical leader for the design, development, and lifecycle management of AI agents that accelerate the delivery of Crowe's Audit services. This role is built around Crowe's forward deployed engineer (FDE) model: rather than sitting above a project as a traditional program manager, this person sits with Audit practitioners, learns the workflow, designs the agent as it is built, and owns the outcome end to end. The goal is to establish the technical patterns, Skills-based architecture, and engineering practices needed to support a growing portfolio of agents. As that portfolio grows, the role is expected to grow into leading a small team as an engineering manager with full-stack engineers who each own a slice of the roadmap.

Key Responsibilities

Delivery of Audit AI Agents

Own the design, development, maintenance, and continuous improvement of AI agents and agent-assisted workflows that accelerate the execution of common tasks across Crowe's Audit practice.
Operate as a forward deployed engineer paired directly with an Audit SME — sitting with practitioners, learning the workflow firsthand, and designing the agent as it is built rather than from a static spec.
Remain hands-on in development: write, review, test, and maintain substantial Python codebases and the Skills, instructions, prompts, and supporting resources that define agent behavior.
Manage project scope, timelines, and stakeholder communication for the agents you own — balancing hands-on delivery with the planning, prioritization, and coordination expected of a senior technical lead.
Design and develop agents using a Skills-based architecture — modular, reusable capabilities that can be efficiently discovered, invoked, tested, maintained, and enhanced over time.
Serve as the technical authority for the agent layer, making key design and implementation decisions while partnering with architects and engineering teams to ensure alignment with the broader Audit technology ecosystem.
Implement and maintain monitoring, observability, and alerting for agents running in production, including trace-level visibility into agent reasoning and tool calls, to proactively detect, diagnose, and resolve issues.

Build and Scale the Engineering Capability

Establish reusable engineering patterns and development practices that let a growing portfolio of agents be developed, evaluated, deployed, supported, and enhanced sustainably.
Build and maintain CI/CD pipelines in Azure DevOps to automate testing, evaluation, and deployment of agents across environments.
As the portfolio grows, lead a small team of forward deployed engineers that advance the roadmap per the audit stakeholders they serve.
Participate in the development and evolution of agent evaluation strategies, including representative test cases, automated evaluators, regression testing, quantifiable accuracy metrics, and processes for identifying and addressing changes in agent behavior.
Mentor engineers and provide technical leadership, including code and design reviews, as the agent portfolio and supporting team grow.
Promote collaboration across the Audit BU and the broader Studio to surface opportunities, share reusable patterns, and avoid duplicated build effort.

Product Judgment

Think in terms of ROI, business case, and value pool for every initiative, defining and tracking KPIs to measure and communicate business impact; make product decisions directly alongside the SME rather than routing through a separate product owner.
Demonstrate a solutions-oriented approach: be proactive, identify solutions independently, initiate activities, and follow through to completion.
Leverage data and insights to shape and defend a point of view aligned to Audit's business needs.
Communicate proficiently with both technical staff and business leaders, articulating complex ideas, strategies, and recommendations in terms each audience can act on.

Required Qualifications

8+ years of professional software engineering experience developing, implementing, and supporting enterprise applications and services.
5+ years of professional Python development experience, with a strong track record of building enterprise-grade applications and services.
Demonstrated ability to write clean, maintainable, modular, testable, and well-structured Python code designed for long-term support and scalability — production-grade software that does not create technical debt.
1+ years of hands-on experience designing, developing, and productionizing AI agents or AI-assisted workflows using a major AI platform or model provider such as Microsoft, Anthropic, or OpenAI.
Hands-on experience developing agents using a Skills-based architecture, including authoring and maintaining Skills definitions, markdown-based instructions, supporting resources, and executable code used by agents.
Experience testing and evaluating AI systems, including automated evaluators, representative test cases, regression testing, and analysis of agent failure modes.
Demonstrated ability to embrace and advocate for AI-assisted software engineering practices, including the effective use of coding assistants (e.g. Claude Code, Codex, GitHub Copilot), automated testing, and design-time AI tools to accelerate solution design, improve quality, and enhance developer productivity.
Demonstrated project management experience — scoping, planning, and managing technical projects/initiatives end-to-end in a fast-paced, cross-functional environment — with a strong track record of proactive, independent problem-solving.
Experience providing technical leadership to software engineering teams, including mentoring engineers, conducting code and design reviews, and establishing engineering practices and standards.
Experience designing and deploying AI/ML solutions on major cloud platforms (AWS, Azure, and/or GCP), including the use of managed AI services, cloud-native integration patterns, scalable data architectures, and secure, enterprise-grade deployments. Azure experience preferred.
Experience with Azure DevOps — including building and maintaining CI/CD pipelines — or a similar platform, and Git-based version control.
Excellent verbal and written communication skills, with the ability to explain complex technical work to non-technical business leaders and translate complex requirements into practical technical solutions alongside product managers, architects, and business stakeholders.

Preferred Qualifications

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent professional experience.
Experience developing software or AI solutions for Audit, accounting, public accounting, financial services, or another regulated industry.
Proficiency with modern AI/ML frameworks, tools, and agent platforms (e.g. MCP, A2A, Microsoft Foundry, OpenAI Agent SDK, Microsoft Agent Framework, Claude SDK, LangGraph, LangChain, LlamaIndex, Microsoft Copilot Studio, or equivalent).
Knowledge of retrieval-augmented generation (RAG), vector databases, embeddings, and techniques for grounding model outputs in enterprise data.
Experience delivering solutions with the Microsoft .NET stack, C#, and SQL Server.
Experience helping establish or scale an engineering capability, including defining development standards, reusable patterns, and practices that support a growing team and portfolio of production solutions.
Consulting, product management, investment banking, or startup experience that has developed strong commercial and product instincts.
Prior experience as a forward deployed engineer or similar embedded technical role, partnering directly with subject matter experts to design and build workflow software without a separate product-owner layer.
Demonstrated passion for sharing what you learn, whether by offering cross-training opportunities, giving internal team "lightning talks," or by writing detailed comments on tickets when you close them.
Demonstrated curiosity and continuous learning mindset in Generative AI and applied machine learning, with an interest in staying current on emerging models, architectures, and techniques, including engaging with original research, technical papers, and pre-release concepts before they are broadly commercialized.

Work Arrangement

Hybrid

Pay

A reasonable estimate of the current range is $102,400.00 - $204,100.00 per year. The wage range takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Crowe, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.

Benefits

Crowe offers employees a comprehensive total rewards package.