Lead platform releases feature rollouts and adoption initiatives in partnership with product and engineering teams.
o Architect and execute go to market strategies spanning onboarding training documentation and ongoing support.
Customer Enablement & Training o Conduct workshops office hours and hands on pair programming while maintaining self service resources (SDKs guides playbooks) to drive adoption and reduce time to value. o Create scalable enablement assets and tailor training approaches based on a deep understanding of customer workflows and pain points.
Solution Strategy & Feedback Loop o Establish tight feedback loops with end users to surface insights that shape roadmap direction influence implementation and drive usability improvements. o Translate business problems into actionable solution architectures partnering with platform teams on patterns reusable accelerators acceptance criteria and reference architectures to standardize solution delivery.
o Stay current with industry trends in MLOps/LLMOps GenAI agentic frameworks and cloud optimization.
Stakeholder Relationship & Communication o Build
Required Qualifications:
4 years of Artificial Intelligence experience or equivalent demonstrated through one or a combination of the following: work experience training military experience education
3 years across product/solution management program delivery or technical product ownership for AI/ML platforms or cloud-native solutions.
3 years hands-on with cloud technologies (GCP or Azure) and container orchestration (Docker Kubernetes/OpenShift). Desired Qualifications
5 years across the AI/ML lifecycle: data management feature engineering model development deployment monitoring/observability and model risk/governance.
Experience in large enterprise environments (regulated industries preferred) and building platforms at scale.
Hands-on with GenAI and agentic AI (LLMs diffusion models RAG tool use/agents); familiarity with OpenAI Azure Hugging Face LangChain/LangGraph ADK vector databases.
Experience with MLOps/LLMOps tooling and practices (model registry CI/CD feature store prompt/chain/versioning evaluation guardrails monitoring).
Strong communication skills with the ability to influence senior stakeholders and simplify complex technical
Regards
Bhupendra
AI Evangelist/AI Expert/ GenAI Solutions Leader Location : Phoenix AZ - Onsite Full-Time Lead platform releases feature rollouts and adoption initiatives in partnership with product and engineering teams. o Architect and execute go to market strategies spanning onboarding training documentation...
AI Evangelist/AI Expert/ GenAI Solutions Leader
Location : Phoenix AZ - Onsite
Full-Time
Lead platform releases feature rollouts and adoption initiatives in partnership with product and engineering teams.
o Architect and execute go to market strategies spanning onboarding training documentation and ongoing support.
Customer Enablement & Training o Conduct workshops office hours and hands on pair programming while maintaining self service resources (SDKs guides playbooks) to drive adoption and reduce time to value. o Create scalable enablement assets and tailor training approaches based on a deep understanding of customer workflows and pain points.
Solution Strategy & Feedback Loop o Establish tight feedback loops with end users to surface insights that shape roadmap direction influence implementation and drive usability improvements. o Translate business problems into actionable solution architectures partnering with platform teams on patterns reusable accelerators acceptance criteria and reference architectures to standardize solution delivery.
o Stay current with industry trends in MLOps/LLMOps GenAI agentic frameworks and cloud optimization.
Stakeholder Relationship & Communication o Build
Required Qualifications:
4 years of Artificial Intelligence experience or equivalent demonstrated through one or a combination of the following: work experience training military experience education
3 years across product/solution management program delivery or technical product ownership for AI/ML platforms or cloud-native solutions.
3 years hands-on with cloud technologies (GCP or Azure) and container orchestration (Docker Kubernetes/OpenShift). Desired Qualifications
5 years across the AI/ML lifecycle: data management feature engineering model development deployment monitoring/observability and model risk/governance.
Experience in large enterprise environments (regulated industries preferred) and building platforms at scale.
Hands-on with GenAI and agentic AI (LLMs diffusion models RAG tool use/agents); familiarity with OpenAI Azure Hugging Face LangChain/LangGraph ADK vector databases.
Experience with MLOps/LLMOps tooling and practices (model registry CI/CD feature store prompt/chain/versioning evaluation guardrails monitoring).
Strong communication skills with the ability to influence senior stakeholders and simplify complex technical