Senior AI Engineer, Applied AI Solutions

Horizon Media•New York, NY

5d•$195,000 - $225,000•Hybrid

About The Position

We are pioneering a new AI development team dedicated to revolutionizing our agency's operations from the ground up. We are looking for an entrepreneurial and deeply technical AI Engineer to be a foundational member of this team. This role sits at the intersection of AI engineering and real world execution. As a Senior AI Engineer, you will design and build agentic systems that translate complex business workflows into automated, scalable solutions. You will work across multiple AI platforms, rapidly prototype solutions, and produce high-impact solutions that create enterprise-wide acceleration and efficiencies. This role requires strong engineering fundamentals, product thinking, and the ability to operate in ambiguity.

Requirements

Proven experience architecting and deploying end-to-end AI/ML systems in production environments, with clear ownership from problem definition through delivery
Deep hands-on expertise in Google Cloud Platform, including production use of Vertex AI (Pipelines, Search, Model APIs) and the Gemini model family
Demonstrated experience designing and building agentic AI systems, including multi-step, tool-using agents and complex RAG architectures; familiarity with frameworks such as LangChain, LlamaIndex, or similar
Strong software engineering fundamentals, including: Python proficiency and modern backend development (APIs, microservices)
Experience with containerization (Docker), orchestration (e.g., GKE), and CI/CD pipelines
Experience building LLM-powered applications that integrate with external tools, APIs, and enterprise data sources
Strong systems thinking mindset, with the ability to design around platform constraints rather than be blocked by them
Demonstrated ability to operate in fast-moving, ambiguous environments with a high degree of ownership and accountability

Nice To Haves

Experience working across multiple AI platforms and systems (e.g., Gemini, Claude, Perplexity) and evaluating tradeoffs between them
Experience integrating AI systems with enterprise tools and data platforms (e.g., Snowflake, BigQuery, SharePoint, Salesforce)
Familiarity with enterprise security models, data access controls, and permission-aware system design
Background in client-facing, consulting, or cross-functional product environments

Responsibilities

Architect and deliver multi-step, tool-using agent systems that automate complex enterprise workflows end-to-end
Partner with business leaders to identify high-friction processes, define requirements, and translate ambiguity into structured, scalable solutions
Decompose workflows into deterministic, automatable components and design agent architectures that drive enterprise-wide efficiency and reuse
Develop orchestration logic across APIs, data sources, and large language models, ensuring reliability, performance, and scalability
Design around platform constraints, leveraging creative engineering approaches to unlock capabilities rather than being limited by them
Build and deploy production-grade systems, including RAG pipelines, tool integrations, and API-driven workflows
Rapidly prototype across AI platforms (e.g., Google Gemini, Claude, Perplexity AI) to validate feasibility and inform platform strategy
Select and integrate the optimal models, tools, and ecosystems based on use case requirements, not platform bias
Lead end-to-end development from zero-to-one prototypes through scaled, production-ready systems
Ensure solutions are extensible, modular, and designed for enterprise adoption
Work directly with cross-functional stakeholders to map workflows, align on outcomes, and operationalize AI-driven transformation
Translate business needs into technical architectures that deliver measurable impact on efficiency, throughput, and quality regularly optimizing for future scale
Collaborate across product, design, data, and engineering teams to ensure solutions are usable, adopted, and outcome-driven
Operate at the forefront of the AI ecosystem, continuously evaluating emerging models, tools, and frameworks
Lead rapid experimentation cycles across LLMs, RAG architectures, and agentic systems, iterating based on performance and user feedback
Champion a culture of applied innovation, balancing speed, rigor, and scalability
Drive the adoption of cutting-edge capabilities to maintain competitive advantage and unlock new enterprise value