Delivery Module Lead

About the role

Go-To-Market Lead – AI Prod Mgr 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 capabilitiesForward-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.

Responsibilities

• 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
• 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
• 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
• 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
• 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
• 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
• 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

Requirements

• 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 (GCP or Azure) and containerization (Docker, Kubernetes/OpenShift)

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,