AI Enablement Specialist

Eurasia Group•Washington, DC

3d•$110,000 - $120,000

About The Position

Eurasia Group is seeking an AI Enablement Specialist to join our IT organization. This role will accelerate responsible, practical adoption of AI tools across the firm by helping teams identify high-value use cases, design workable AI-enabled processes, and build lightweight automations that fit real workflows. The AI Enablement Specialist will be primarily a facilitator and adoption driver—someone who can translate between technical and non-technical stakeholders, run structured engagements, and deliver repeatable enablement assets (training, documentation, patterns, and playbooks). While not a software engineering role, the Specialist should be comfortable with light coding and technical troubleshooting (e.g., debugging an API call, building a small automation, or diagnosing why a workflow isn’t working). This is a full-time role based in the New York, Washington DC, or London (with flexibility for other office locations).

Requirements

1-2 years working with AI/LLM tools in a professional setting (e.g., prompt engineering, workflow design, tool evaluation).
Some coding experience, sufficient to debug an API call, write a simple automation, or diagnose why something isn’t working (Python or JavaScript preferred; not required to be production-grade). Proficient with Markdown format.
Genuine knowledge and curiosity about the AI landscape: keeps up with model releases/popular AI apps (beyond just ChatGPT and Claude); can articulate differences between challenging AI concepts, e.g., Reasoning vs. Non-Reasoning models, Context Length vs. Context Rot, RAG vs. fine-tuning, etc.
Demonstrated project management across multiple concurrent workstreams (formal PM certification not required; must show portfolio-level thinking).
Strong communication and “consulting” instincts—ability to run meetings, translate between technical and non-technical stakeholders, and write clear documentation.

Nice To Haves

Experience in professional services or other knowledge-work environments (e.g., consulting, law, finance, policy) and comfort partnering with highly analytical stakeholders.
Experience with enterprise AI platforms (e.g., Cassidy, Microsoft Copilot, custom GPTs).
Training/teaching experience and comfort delivering enablement to groups.
Change management background (formal or informal).

Responsibilities

Drive AI adoption through enablement and workflow integration
Partner with teams across Eurasia Group (research, consulting, business teams, and operations) to understand their workflows and identify AI opportunities grounded in business needs—not novelty.
Translate ambiguous requests (“we want AI to do X”) into achievable technical options, or explain limitations and tradeoffs.
Build lightweight AI solutions and prototypes
Design and implement small, maintainable automations and AI-enabled workflows where appropriate (e.g., iterating carefully on complex prompts, connecting MCPs and APIs, constructing workflows in our visual AI workflow builder, simple scripts).
Troubleshoot and debug issues across AI tools and workflows, escalating appropriately when deeper engineering is required.
Run multiple concurrent initiatives with strong project discipline
Manage a pipeline of AI enablement projects across departments, coordinating timelines, stakeholders, and deliverables.
Proactively communicate with stakeholders to keep them updated without waiting for them to reach out.
Know when to escalate or push back when facing scope-creep, poor prioritization, and other stakeholder management issues.
Maintain clear documentation of projects and status to ensure nothing is lost in the shuffle.
Run pilot programs for new AI-related apps for employee use, including making the business case, identifying employees as test users, setting objectives, evaluating success, and assisting in rollout
Create scalable enablement assets (and avoid becoming a help desk)
Develop training materials, documentation, and “how-to” guides that help teams self-serve
Establish a “champions model” (or similar) to identify AI leads within departments to provide first-line support and local momentum
Create office hours (or similar processes) that funnel questions into manageable time blocks (rather than becoming an “on-call” tech support resource)