Customer Success Lead

Customer Success Lead Location: Charlotte, NC (Hybrid) In this role, you will serve as the connective tissue between product, engineering, line-of-business stakeholders, and end usershelping teams adopt AI capabilities, integrate agentic workflows, and scale enablement into repeatable, self-service models. Ideal candidates bring strong engineering fundamentals, deep customer empathy, and a teachers mindsetsomeone who enjoys building solutions, enabling others, and turning individual learnings into scalable programs and product improvements. Youll thrive here if youre hands-on, fast-moving, and energized by dynamic environments where you both build and teach, deliver and scale. Key Responsibilities Product Go-to-Market Lead platform releases, feature rollouts, and adoption initiatives in partnership with product and engineering teams. Architect and execute go-to-market strategies spanning onboarding, training, documentation, and support. Own the rollout and adoption of new platform features and capabilities across stakeholder groups. Track KPIs such as adoption, reliability, latency, and business impact to guide continuous improvement. Translate platform updates into clear, actionable guidance for both technical and non-technical audiences. Customer Enablement & Training Lead interactive enablement programsincluding live workshops, pair-programming sessions, office hours, and hands-on technical engagements. Help teams integrate AI tools and capabilities into day-to-day development practices. Create and maintain enablement materials, SDK resources, best practices, and repeatable playbooks. Scale individual training engagements into enterprise-wide digital learning content and self-service models. Build a deep understanding of customer pain points and workflows to tailor adoption strategies and reduce time-to-value. Product Strategy & Feedback Loop Establish tight feedback loops between end-users, product, and engineering teamscapturing insights that meaningfully inform roadmap decisions. Translate customer needs into product direction: define requirements, scope solutions, and influence implementation. Advocate for usability improvements and enhancements grounded in field observations and customer experience. Validate fixes, test new features, and conduct quality checks to proactively surface gaps in stability or performance. Maintain empathy for how developers work and champion features that improve the end-user experience. Stakeholder Relationship & Communication Build strong relationships with business stakeholders, end users, and product teams; serve as a trusted technical advisor. Communicate complex technical concepts clearly and with nuance across diverse audiences. Produce executive-ready updates, metrics, narratives, and adoption insights. Governance, Security & Compliance Ensure alignment with model governance, responsible AI practices, data protection, and platform security controls. Contribute to documentation and artifacts for validation, testing, approvals, and audit readiness. Partner with risk, compliance, and security teams to ensure safe and compliant delivery of AI capabilities. Required Qualifications Bachelors degree in a STEM field or equivalent hands-on experience. 4+ years in artificial intelligence solutions, AI consulting, product/solution management, program delivery, or technical product ownership for AI/ML platformsor equivalent experience delivering AI/ML outcomes at scale. Strong coding proficiency (Python, JavaScript/TypeScript, or similar). Proven ability to communicate complex technical topics to diverse audiences. Strong customer-service orientation and comfort facilitating interactive sessions and workshops. Demonstrated ability to learn and adapt quickly in ambiguous, fast-changing environments. Strong technical acumen with the ability to build deep relationships with engineering and business stakeholders. Customer empathy and a bias toward executionquickly identifying solutions and advocating for improvements to meet user needs. Desired Qualifications Education: Masters degree or higher in computer science, engineering, data science, or a related field. Technical Expertise: Hands-on experience developing or deploying GenAI or agentic AI systems (LLMs, RAG, tools/agents, orchestration frameworks). Familiarity with AI/ML platforms and cloud infrastructure (Azure, AWS, GCP). Experience with MLOps/LLMOps practices (model registry, CI/CD, evaluation, guardrails, monitoring). Proficiency with OpenAI, Azure OpenAI, Hugging Face, LangChain/LangGraph, vector databases, and related frameworks. Experience integrating LLM-powered workflows or agent-based systems into production environments. Customer Enablement Experience: Experience leading technical workshops, internal enablement programs, developer onboarding, or training initiatives. Background in product management, technical product ownership, customer success, or technical program delivery. Ability to distill complex topics into clear playbooks, documentation, and learning materials. Familiarity with collaboration and workflow tools such as Jira, Confluence, and ServiceNow.