AI Technical Lead

Crowe Advisory LLC•Dallas, TX

6d•$102,400 - $204,100•Hybrid

About The Position

Crowe’s Studio team is seeking a hands-on technical leader to design, develop, and manage the lifecycle of AI agents that accelerate the delivery of Crowe’s Audit services. The role follows a forward deployed engineer model: working directly with Audit practitioners and subject matter experts to understand workflows, design agents alongside users, and own outcomes end to end. The role will establish technical patterns, a Skills-based architecture, and engineering practices for a growing portfolio of AI agents. As the portfolio grows, the position may lead a small team of forward deployed engineers, with each engineer owning part of the roadmap. The role combines substantial Python development with project planning, stakeholder communication, technical decision-making, production monitoring, and agent evaluation. It also includes establishing reusable development practices, CI/CD pipelines, and evaluation strategies to support sustainable development and deployment. The technical lead will make product decisions with Audit subject matter experts, measure business value, and communicate recommendations to technical and business audiences. Crowe describes its AI work as using machine learning, generative AI, modern architectures, and industry expertise to build AI-powered solutions. The position is hybrid. Crowe expects employees to uphold its values of Care, Trust, Courage, and Stewardship and to act ethically and with integrity. Crowe is not sponsoring work authorization at this time. The stated annual wage range is $102,400 to $204,100, subject to factors including skills, experience, training, and location.

Requirements

At least 8 years of professional software engineering experience developing, implementing, and supporting enterprise applications and services.
At least 5 years of professional Python development experience building enterprise-grade applications and services.
Ability to write clean, maintainable, modular, testable, well-structured Python code designed for long-term support and scalability.
At least 1 year 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.
Experience testing and evaluating AI systems, including automated evaluators, representative test cases, regression testing, and analysis of agent failure modes.
Ability to embrace and advocate for AI-assisted software engineering practices, including coding assistants, automated testing, and design-time AI tools.
Project management experience scoping, planning, and managing technical projects or initiatives end to end in a fast-paced, cross-functional environment.
Strong proactive, independent problem-solving skills.
Experience providing technical leadership to software engineering teams, including mentoring, code and design reviews, and establishing engineering practices and standards.
Experience designing and deploying AI/ML solutions on AWS, Azure, and/or GCP, including managed AI services, cloud-native integration patterns, scalable data architectures, and secure enterprise-grade deployments.
Experience with Azure DevOps CI/CD pipelines or a similar platform, and Git-based version control.
Excellent verbal and written communication skills, including explaining technical work to non-technical business leaders and translating requirements into practical technical solutions with product managers, architects, and business stakeholders.

Nice To Haves

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 such as MCP, A2A, Microsoft Foundry, OpenAI Agent SDK, Microsoft Agent Framework, Claude SDK, LangGraph, LangChain, LlamaIndex, or Microsoft Copilot Studio.
Knowledge of retrieval-augmented generation (RAG), vector databases, embeddings, and methods for grounding model outputs in enterprise data.
Experience delivering solutions with the Microsoft .NET stack, C#, and SQL Server.
Experience establishing or scaling an engineering capability, including defining development standards, reusable patterns, and practices for a growing team and production portfolio.
Consulting, product management, investment banking, or startup experience that developed commercial and product instincts.
Prior experience as a forward deployed engineer or in a similar embedded technical role, partnering directly with subject matter experts to design and build workflow software without a separate product-owner layer.
Interest in sharing knowledge through cross-training, internal lightning talks, or detailed ticket comments.
Curiosity and a continuous-learning mindset in Generative AI and applied machine learning, including interest in emerging models, architectures, techniques, research, technical papers, and pre-release concepts.

Responsibilities

Design, develop, maintain, and continuously improve AI agents and agent-assisted workflows for common tasks across Crowe’s Audit practice.
Work directly with Audit subject matter experts as a forward deployed engineer, learning workflows firsthand and designing agents during development.
Write, review, test, and maintain substantial Python codebases and the Skills, instructions, prompts, and supporting resources that define agent behavior.
Manage scope, timelines, and stakeholder communications for owned agents while balancing hands-on delivery with planning, prioritization, and coordination.
Design and develop agents using a Skills-based architecture with modular, reusable capabilities that can be discovered, invoked, tested, maintained, and enhanced.
Serve as the technical authority for the agent layer and make design and implementation decisions in partnership with architects and engineering teams.
Implement and maintain production monitoring, observability, and alerting, including trace-level visibility into agent reasoning and tool calls.
Establish reusable engineering patterns and practices to support the sustainable development, evaluation, deployment, support, and enhancement of agents.
Build and maintain Azure DevOps CI/CD pipelines to automate agent testing, evaluation, and deployment across environments.
As the portfolio grows, lead a small team of forward deployed engineers advancing the roadmap in partnership with Audit stakeholders.
Help develop and evolve agent evaluation strategies, including representative test cases, automated evaluators, regression testing, accuracy metrics, and processes for identifying and addressing behavior changes.
Mentor engineers and provide technical leadership through code and design reviews as the portfolio and team grow.
Promote collaboration across the Audit business unit and Studio to surface opportunities, share reusable patterns, and avoid duplicated effort.
Assess initiatives in terms of ROI, business case, and value; define and track KPIs, and make product decisions directly with subject matter experts.
Proactively identify solutions, initiate activities, and follow through to completion.
Use data and insights to shape and defend recommendations aligned with Audit’s business needs.
Communicate complex ideas, strategies, and recommendations effectively to technical staff and business leaders.