Lead Agentic & Gen AI engineer with Go-To-Market Lead

Good with Relocation

Role : Agentic & Gen AI Go-To-Market Lead

Location : Minneapolis, MN / Charlotte, NC / Irving, TX (Hybrid)

Contract

End Client - Service base industry

Experience - 10+ Years

About this role:

seeking a Go-To-Market Lead to join the Digital Technology & Innovation (DTI) organization. DTI supports Corporate Strategy, Digital Platforms & Innovation, working to advance enterprise AI capabilities and accelerate the integration of cutting-edge innovation into customer-facing products and internal platforms.

This role sits at the intersection of product, engineering, and go-to-market (GTM) and is designed for a hybrid leader–builder: someone who not only shapes strategy but also gets hands-on building prototypes, coding solutions, and rapidly demonstrating value.

We are seeking a highly technical, product-oriented, and customer-focused individual to drive adoption and delivery of Generative AI and agentic AI capabilities across the enterprise. This individual will function as a blend of:

•                     Go-To-Market Leader – defining and executing adoption strategies for AI platforms and capabilities

•                     Forward-Deployed Engineer – working directly with business teams to prototype, build, and deploy AI-powered solutions in real-world workflows

You will play a critical role in turning emerging AI capabilities into tangible business impact, partnering closely with product, engineering, and line-of-business stakeholders to accelerate experimentation, deployment, and scale.

This role requires a strong bias for action, comfort with ambiguity, and the ability to leverage modern AI-native development tools to move from idea to working solution rapidly.

In this role, you will:

Go-To-Market Leadership & Adoption

•                     Define and execute GTM strategies for enterprise AI capabilities, with emphasis on LLMs, agentic systems, and composable AI platforms

•                     Drive adoption of APIs, agent frameworks, orchestration layers, and developer tools through targeted enablement and engagement

•                     Lead feature launches and platform rollouts, translating technical capabilities into clear business value narratives

Forward-Deployed Engineering & Solution Acceleration

•                     Partner directly with business and engineering teams to design, prototype, and deploy GenAI and agentic solutions

•                     Build hands-on demos, reference implementations, and rapid prototypes that showcase platform capabilities in real-world use cases

•                     Engage in pair programming, debugging, and solution development, accelerating time from concept to production

•                     Leverage tools such as GitHub Copilot, Devin, Cursor, and other AI-assisted development platforms to rapidly solve problems and iterate

Customer Enablement & Developer Experience

•                     Lead workshops, office hours, and hands-on sessions focused on building with LLMs, RAG architecture, and agentic workflows

•                     Develop scalable enablement assets (SDKs, playbooks, reusable components, prompt libraries, agent templates)

•                     Improve developer experience and time-to-value by identifying friction points and driving improvements across tooling and documentation

Solution Strategy & Agentic Architecture

•                     Translate business problems into GenAI and agentic solution architectures, incorporating patterns such as RAG, tool use, multi-agent orchestration, and memory frameworks

•                     Partner with platform teams to define reusable design patterns, accelerators, and reference architectures

•                     Stay at the forefront of GenAI, agentic systems, LLMOps, and emerging AI-native development paradigms

Feedback Loop & Product Influence

•                     Establish tight feedback loops with end users to shape platform roadmap and prioritize enhancements

•                     Capture insights across deployments to inform improvements in usability, performance, and scalability of AI solutions

•                     Champion a customer-first, experimentation-driven culture across AI initiatives

Stakeholder Engagement & Communication

•                     Act as a trusted advisor bridging technical depth and business strategy

•                     Communicate complex AI concepts clearly to both technical and non-technical audiences

•                     Deliver executive-ready updates highlighting

•                     adoption, business impact, and innovation outcomes

Governance, Security & Responsible AI

•                     Ensure adherence to responsible AI principles, model governance, data security, and regulatory requirements

•                     Collaborate with risk, compliance, and security teams to operationalize safe and compliant AI deployments

Required Qualifications

•                     4+ years of experience in Artificial Intelligence experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

•                     3+ years in go-to-market leadership, technical product, solution engineering, or forward-deployed engineering roles

•                     Hands-on experience building with GenAI/LLMs and agent-based systems, including rapid prototyping and deployment

•                     Strong programming skills and experience working with modern development stacks and APIs

•                     3+ years of experience with cloud platforms (Google Cloud Platform or Azure) and containerization (Docker, Kubernetes/OpenShift)

Desired Qualifications

Deep expertise in Generative AI and agentic architectures, including: LLMs, RAG, embeddings, vector search

Agent frameworks (LangChain, LangGraph, AutoGen, ADK, or similar)

Tool use, orchestration, and multi-agent systems

Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Devin, Cursor) to accelerate innovation and delivery

Strong familiarity with LLMOps practices: prompt/version management, evaluation, observability, guardrails

Experience building end-to-end AI applications, from prototype to production

Proven ability to build demos, prototypes, and customer-facing solutions that drive adoption

Experience in large-scale enterprise environments, preferably in regulated industries

Strong communication and stakeholder management skills with ability to influence senior leaders

Ability to operate as both a strategic leader and hands-on builder