Title: AI Tech Lead / Senior AI Engineer
Location: Chicago, Illinois
Work Arrangement: Hybrid (3 Days Onsite) or Remote
Employment Type: Full-Time, Contract-to-Hire (W-2)
Work Authorization: U.S. Citizen or Green Card Holder

Role Overview
Client is building the next generation of enterprise AI engineering capabilities and is seeking experienced AI Tech Leads and Senior AI Engineers who thrive at the intersection of innovation, software engineering, and client delivery. This is a highly visible, hands-on role responsible for helping enterprise clients transform how software is designed, developed, tested, deployed, and supported using Artificial Intelligence. The successful candidate will work directly with client executives, architects, and engineering teams to build production-ready AI solutions that deliver measurable business outcomes. Rather than simply advising on AI strategy, this individual will lead by building, mentoring, and executing alongside delivery teams while establishing Client as a trusted AI transformation partner.

Objectives
1. Deliver Enterprise AI Solutions That Produce Measurable Business Results
Within the first six months, lead the design, development, testing, deployment, and operational support of production-ready AI solutions that improve software engineering productivity, automate manual processes, accelerate go-to-market initiatives, and enhance operational performance. Partner directly with client stakeholders to ensure every solution delivers measurable business value through increased efficiency, reduced delivery time, improved quality, or lower operating costs. Success will be measured by successful production deployments, client adoption, engineering productivity improvements, and documented business outcomes.

2. Transform the Software Development Lifecycle Through AI
Lead the integration of AI throughout the complete software development lifecycle, identifying opportunities where AI can improve solution design, software development, code quality, testing, deployment, monitoring, incident response, documentation, and production support. Build reusable frameworks that enable engineering teams to consistently deliver higher-quality software faster and more efficiently. Success will be measured through measurable reductions in development cycle time, increased automation, improved engineering quality, and expanded adoption of AI engineering practices.

3. Build Trusted Executive Relationships While Leading Technical Delivery
Serve as the primary technical advisor during client engagements by facilitating discovery workshops, translating business challenges into scalable AI solutions, leading technical delivery teams, and communicating effectively with both engineering organizations and executive stakeholders. Establish Client as a trusted partner capable of delivering enterprise AI transformation through execution, innovation, and measurable business impact. Success will be measured through client satisfaction, repeat business, executive confidence, and successful project delivery.

4. Expand Client Enterprise AI Practice
Create reusable architectures, engineering accelerators, implementation patterns, reference solutions, and client success stories that strengthen Client AI consulting capabilities. Demonstrate how AI solutions have automated manual work, improved software delivery, enabled intelligent self-healing capabilities, accelerated client innovation, and created measurable competitive advantages. Success will be measured through reusable intellectual property, successful client demonstrations, expanded AI opportunities, and increased market credibility.

Critical Subtasks
1. Design Enterprise AI Architectures
Design scalable AI solutions that integrate modern large language models, AI agents, enterprise APIs, and intelligent automation into client environments. Evaluate architectural alternatives and recommend solutions that balance scalability, security, maintainability, and business value.

2. Build Production-Ready AI Applications
Remain actively involved in software engineering throughout the complete development lifecycle by building, reviewing, testing, deploying, monitoring, and supporting enterprise AI applications that meet production standards.

3. Leverage Modern AI Engineering Platforms
Utilize leading AI-native engineering platforms including Google Gemini, Cursor, Claude, Devin, and related AI development technologies to improve developer productivity, automate repetitive engineering activities, accelerate delivery, and improve software quality across client engagements.

4. Lead Client Discovery and Solution Design
Work directly with business and technology leaders to identify opportunities where AI can solve meaningful business problems. Facilitate discovery sessions, define solution roadmaps, develop implementation strategies, and align technical recommendations with business priorities.

5. Mentor and Elevate Engineering Teams
Provide technical leadership, coaching, architectural guidance, and hands-on support to engineering teams while fostering adoption of AI engineering best practices, continuous learning, and technical excellence.

6. Demonstrate Business Value Through Client Success
Develop measurable client success stories that demonstrate how AI has automated manual processes, improved engineering efficiency, reduced operational costs, enhanced customer experiences, or accelerated product delivery. Use these outcomes to support future client engagements and strengthen Client market position.

7. Continuously Evaluate and Integrate Emerging AI Technologies
Continuously assess emerging AI models, AI engineering platforms, intelligent automation technologies, and software development capabilities to identify opportunities that improve client outcomes and engineering productivity. Lead pilot initiatives, validate new technologies, and recommend practical innovations that keep both Client and its clients at the forefront of enterprise AI adoption.

Preferred Technical Environment
Candidates should demonstrate hands-on experience using modern AI engineering tools and practices, including:

• Google Gemini
• Cursor AI
• Claude / Claude Code
• Devin AI
• Python
• AI Agents
• Model Context Protocol (MCP)
• Retrieval-Augmented Generation (RAG)
• Enterprise API Integration
• GitHub and AI-assisted software development
• Modern CI/CD practices

Experience with cloud platforms is beneficial but secondary to demonstrated success building and delivering enterprise AI solutions using AI-native engineering practices.

Definition of Success
After the first year, this individual is recognized by Client leadership and enterprise clients as a trusted AI engineering leader who consistently delivers production-ready AI solutions, transforms software engineering through AI, develops lasting client relationships, and contributes reusable assets that strengthen Clients leadership in enterprise AI consulting.