Enterprise AI Enablement Lead

Jackson Healthcare•Alpharetta, GA

13h•Onsite

About The Position

The Enterprise AI Enablement Lead is a senior individual contributor responsible for designing, building, and operationalizing AI- and agent-enabled solutions that support real business workflows across Jackson Healthcare and its companies. This role goes beyond experimentation or associate education. It requires applied value driven use case assessment, problem-solving, architectural judgment, and hands-on execution to translate emerging AI capabilities into secure, scalable, and reusable enterprise solutions. The Enterprise AI Enablement Lead serves as a subject matter expert in agentic AI application, partners closely with business leaders, infrastructure, identity, data, and application teams, and helps establish the technical and operational foundation required for responsible AI adoption at scale.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field — OR — equivalent combination of education and experience.
7+ years of overall technology experience with demonstrated hands-on responsibility for production-grade solutions.
Demonstrated experience leading the identification, evaluation and selection of business processes and workflows for AI enablement across multiple business or knowledge domains.
Proven experience developing and deploying AI-driven or automation-enabled solutions in enterprise environments.
Strong understanding of software architecture, system integration, and SDLC practices.
Demonstrated ability to evaluate and apply modern AI tooling and agent orchestration concepts responsibly.
Experience designing agentic or workflow-embedded AI solutions.
Familiarity with identity, authorization, and governance considerations in automated systems and modern AI architecture.
Hands-on experience integrating AI capabilities into existing platforms.
Experience working in multi-subsidiary or large enterprise environments with distributed governance and operational complexity.
Familiarity with AI governance frameworks such as Gartner AI TRiSM, NIST AI RMF, or comparable structured approaches to responsible AI deployment.
Hands-on experience with prompt engineering, retrieval-augmented generation (RAG), and evaluation techniques for production AI systems.
Demonstrated experience enabling non-technical stakeholders or citizen developers to adopt AI tools and agent capabilities responsibly.

Nice To Haves

Experience in healthcare, healthcare staffing, or other regulated industries.
Hands-on experience with Microsoft 365 Copilot, Azure AI Services, or Salesforce AI capabilities (e.g., Agentforce, Einstein).
Experience with agent orchestration frameworks, Model Context Protocol (MCP), or LLM gateway and API management patterns.
Exposure to enterprise platforms such as ServiceNow, SAP, or large-scale CRM environments in the context of AI integration.
Understanding of HIPAA, SOC 2, or comparable compliance requirements as they apply to AI-enabled systems.
Relevant certifications such as Azure AI Engineer Associate, AWS Machine Learning Specialty, or equivalent.

Responsibilities

Identify and translate ambiguous business problems and process opportunities into implementable AI solutions with clear ownership, outcomes, and operational constraints.
Design, build, and deploy AI- and agent-enabled solutions embedded in real enterprise workflows across multiple business lines.
Define and apply practical reference architectures for agentic AI, including task orchestration, decision boundaries, escalation patterns, and human-in-the-loop controls.
Evaluate and apply no-code, low-code, and full-code approaches appropriately based on risk, scale, and maturity requirements.
Lead hands-on implementation of agentic AI patterns, including multi-step reasoning, tool use, and workflow coordination.
Partner with application and platform engineers to integrate AI agents with enterprise systems, automation platforms, and data sources.
Establish clear distinctions between experimentation, pilot solutions, and enterprise-ready deployments.
Enable responsible citizen self-service agent development where appropriate by promoting awareness, fluency, and enthusiasm and helping to create the necessary foundational scaffolding to support it.
Partner with identity, security, and governance teams to embed execution guardrails directly into AI solution design.
Ensure AI agents operate within defined authorization, least-privilege, and auditability constraints.
Contribute to the development of standards, patterns, and documentation that support secure and governed AI deployment.
Convert successful AI implementations into reusable patterns, templates, and guidance for broader enterprise adoption.
Enable other technical teams and business partners through shared artifacts, examples, and applied guidance.
Support cross-functional initiatives where AI capabilities intersect with infrastructure, data, automation, and application platforms.
Communicate AI solution designs, constraints, and outcomes clearly to technical teams, business partners, users, and leadership.
Serve as a technical advisor on AI capabilities and limitations across enterprise initiatives.
Participate in architectural and change reviews to assess AI impact on systems, workflows, and risk posture.