AI Technical Lead

Kutir Technologies; Chicago (IL); On-site; USD 150,000 - 190,000; Full time

Job summary

Kutir Technologies in Chicago seeks a Technical Lead-L4 to drive enterprise AI delivery. This hands-on role partners with client executives, architects, and delivery teams to design, build, test, deploy, and support production AI solutions delivering measurable business outcomes.

You will mentor engineers, shape architectures, lead discovery sessions, and ensure scalable AI platforms using modern AI tooling and CI/CD practices in a fast-paced enterprise setting.

Qualifications

• Hands-on experience with modern AI engineering tools and platforms.
• Experience leading AI delivery for enterprise clients.
• Strong architectural design and prototyping skills.

Responsibilities

• Design scalable AI architectures integrating LLMs, agents, and enterprise APIs.
• Lead production-ready AI applications from design to deployment.
• Facilitate discovery sessions, map solutions to business outcomes.
• Mentor delivery teams and ensure high-quality engineering practices.

Tools

Google Gemini

Cursor AI

Claude

Claude Code

Devin AI

Python

AI Agents

MCP

RAG

Enterprise API

GitHub AI

CI/CD

Job description

Position Title: Technical Lead-L4

Location: Chicago IL (Onsite)

Duration: 6+ months

Job Description:

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:

Deliver Enterprise AI Solutions That Produce Measurable Business Results 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.

Transform the Software Development Lifecycle Through AI Build reusable frameworks that enable engineering teams to consistently deliver higher-quality software faster and more efficiently.

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.

Subtasks

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.

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.

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.

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:

On day one, 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.