(1114575) Generative AI Adoption Specialist

Diversified Services Network•Irving, TX

20h•Onsite

About The Position

Diversified Services Network, Inc. (DSN) is seeking a full-time Generative AI Adoption Specialist to join its fully onsite team in Irving, Texas. This individual contributor helps business teams understand, adopt, and gain value from Generative AI tools and agentic capabilities. Working with the Data Science Pod Lead, AI Product Owners, AI Architects, and business partners, the specialist translates business needs into practical use cases, guides experimentation and adoption, and connects users with approved tools, platforms, and specialist support. Solutions must align with enterprise standards, security requirements, and Responsible AI principles. The role engages business stakeholders to discover needs, identify opportunities, evaluate potential approaches, and guide recommendations. It also explains GenAI concepts and capabilities to technical and non-technical audiences, creates practical examples and reusable patterns, directs users to appropriate resources, and collaborates with specialist teams. The specialist supports users by troubleshooting tool and workflow issues, documenting resolutions, and escalating issues when appropriate.

Requirements

Bachelor’s degree at minimum; preferably in computer science, engineering, information systems, statistics, mathematics, or a related quantitative field.
At least 2 years of professional experience.
Working knowledge of GenAI tools and business productivity technologies, including the ability to configure, test, demonstrate, troubleshoot, and document prompts, assistants, agents, and workflows.
Hands-on skill in prompt design, system instructions, knowledge grounding, agent configuration, structured outputs, and output evaluation.
Solid understanding of AI, GenAI, and agentic concepts, risks, opportunities, and limitations, and their responsible application to business outcomes.
Digital literacy across collaboration platforms, workflow tools, data sources, APIs, and basic scripting or analytical tools.
Strong verbal and written communication skills.
Patient, self-motivated, collaborative team player.

Nice To Haves

Master’s degree in a technical or quantitative field.
Knowledge of the AI-enabled solution lifecycle, from discovery and use-case definition through validation, approval, deployment, measurement, and retirement.
Experience with structured delivery and governance methods for GenAI solutions, including intake, prioritization, testing, risk review, change management, release, and benefits tracking.
Python programming experience, including NumPy and pandas, applied to business challenges.
Experience with cloud technologies such as AWS, Azure, or Google Cloud.

Responsibilities

Align business engagement and enablement activities with the pod’s direction, priorities, governance, and expected outcomes.
Partner with business stakeholders to understand user needs, desired outcomes, adoption barriers, success measures, and value hypotheses.
Identify and document business problems, process opportunities, and potential applications of GenAI tools or agentic capabilities.
Evaluate opportunities from user, business value, data, risk, governance, technology, adoption, and support perspectives.
Develop and compare approaches, including enterprise tools, configurable agents, reusable patterns, process changes, or referrals to specialist delivery teams.
Use discovery, facilitation, experimentation, and evaluation techniques to guide recommendations and adoption decisions.
Explain GenAI and agentic concepts, use cases, tool capabilities, limitations, risks, and human oversight to technical and non-technical audiences.
Demonstrate how copilots, assistants, agents, knowledge grounding, prompts, and AI-enabled workflows can support business activities.
Direct business users to enterprise guidance, learning resources, approved tools, and specialist support.
Create and share practical examples, reusable patterns, and best practices for responsible and effective GenAI adoption.
Collaborate with architecture, platform, data, engineering, cybersecurity, legal, privacy, Responsible AI, learning, and change-management teams as needed.
Reproduce user issues, clarify expected outcomes, and distinguish user guidance issues from configuration, data, platform, or integration problems.
Answer questions about tool features, prompt behavior, knowledge grounding, access, compatibility, workflow design, and appropriate usage.
Analyze prompts, configurations, outputs, diagnostics, logs, test cases, and user feedback to troubleshoot issues, escalating to specialist teams when needed.
Document and communicate issues, resolution steps, workarounds, and lessons learned to improve ongoing adoption and support.