VP, AI (Agentic Platforms & Transformation)

Horizon Media•New York, NY

1d•$240,000 - $290,000•Hybrid

About The Position

We are building an applied AI function focused on transforming how work gets done across the enterprise through agentic systems, workflow redesign, and intelligent data integration. We are seeking a VP, AI to lead this transformation end-to-end. This role owns how AI is identified, evaluated, built, and scaled within the organization’s workflows, moving from fragmented experimentation to a structured, repeatable system that delivers measurable business impact. You will operate across platforms, define how agents interact with enterprise systems and data, and establish the operating model required to scale adoption. This is a systems leadership role requiring a balance of strategy, product thinking, and execution. You will translate ambiguous business problems into deployable solutions, drive platform and architecture decisions, and build the frameworks that embed AI into day-to-day operations.

Requirements

12+ years of experience across AI, engineering, data, or technical product leadership
Proven track record of deploying AI-driven systems in enterprise environments
Strong understanding of LLM/agent architectures, orchestration patterns (RAG, tool use, multi-agent systems), and API-driven integrations
Experience making architecture decisions across AI platforms, including tradeoffs between RAG, direct API access, and hybrid approaches
Experience translating ambiguous business problems into scalable system architectures and production-grade AI solutions
Demonstrated ability to lead cross-functional teams and drive large-scale initiatives
Strong executive presence with the ability to influence senior stakeholders

Nice To Haves

AI is embedded into core workflows across the organization, not siloed tools
A repeatable system exists to move from idea → build → adoption → scale
Agents and workflows are reusable, scalable, and integrated into real operations
Enterprise data is governed, partitioned, accessible and usable in real time for AI-driven decision-making
AI initiatives consistently deliver measurable business impact at scale
Brings clear, structured recommendations, not open-ended questions
Translates complex workflows into scalable systems, not one-off solutions
Makes strong platform and architecture decisions with clear tradeoffs
Balances speed (POCs) with long-term scalability
Operates with full ownership across strategy, execution, and outcomes

Responsibilities

Lead structured evaluation of AI platforms (e.g., Gemini, Claude, Perplexity), identifying strengths, limitations, and integration pathways
Translate platform capabilities and constraints into clear prioritization for enterprise-wide adoption
Identify and scale high impact use cases tied to measurable business outcomes
Design and scale multi-step, agent-driven workflows that automate and augment core business processes
Translate complex, ambiguous workflows into structured, automatable systems
Ensure AI is embedded into core operations, not deployed as isolated tools
Establish reusable patterns to scale beyond one-off solutions
Drive consistency across teams while maintaining speed and flexibility
Define how third-party systems (SaaS platforms, databases, APIs) integrate into AI workflows
Establish scalable ingestion and integration patterns working with Infrastructure and Architecture Leadership throughout the organization (APIs, connectors, BigQuery, MCP, etc.)
Ensure data is structured, accessible, and governed for AI consumption
Own adoption of AI-driven workflows across the organization
Ensure all AI initiatives are tied to clear, quantifiable outcomes
Drive initiatives from POC → production → sustained usage
Define and track success metrics, including: Workflow adoption, Time saved / efficiency gains, Throughput and decision velocity, Business impact (cost, revenue, productivity)
Define and track success metrics across all AI initiatives, including: Decision velocity improvements, Productivity and output lift
Establish baseline metrics prior to deployment and continuously measure post launch impact
Ensure all AI solutions are tied to clear, quantifiable business outcomes
Redesign business processes to embed AI into daily operations
Establish and refine frameworks for intake, prioritization, and scaling of AI initiatives
Track engineering velocity, output, and impact across workstreams
Build and lead a high-performing team of AI engineers and integration specialists
Establish standards for agent design, orchestration, and integration
Ensure high-quality execution across POCs and production systems
Partner with TPMs and engineering leadership to drive structured delivery
Act as the bridge between business, engineering, and platform teams
Present clear, opinionated recommendations to senior leadership
Drive alignment and best practices across the enterprise
Engage with external partners (e.g., Google) to accelerate innovation and influence roadmap direction