Go To Market Lead

Responsibilities:

1.           Go-To-Market Leadership & Adoption

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

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

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

2.           Forward-Deployed Engineering & Solution Acceleration

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

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

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

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

3.           Customer Enablement & Developer Experience

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

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

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

4.           Solution Strategy & Agentic Architecture

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

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

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

5.           Feedback Loop & Product Influence

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

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

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

6.           Stakeholder Engagement & Communication

1.           Act as a trusted advisor bridging technical depth and business strategy

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

3.           Deliver executive-ready updates highlighting adoption, business impact, and innovation outcomes

7.           Governance, Security & Responsible AI

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

2.           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