BS/BA in Computer Science or related field preferred
8+ years of experience in relevant roles
Strong background in AI and agent development
Relevant certifications in AWS, Azure or cloud AI services preferred
Experience in the Media and Broadcast domain is a plus.
Responsibilities
Define and own AI solution architectures for agentic workflows and infrastructures.
Lead design and deployment of complex RAG architectures and database strategies.
Architect and implement MCP servers and tool integrations with a focus on functionality.
Drive integration with model providers and establish standards for model management.
Establish patterns for AI observability and robust evaluation frameworks.
Collaborate with cross-functional teams to deliver integrated AI solutions.
Guide prototyping efforts and define evaluation criteria for AI tools.
Communicate technical solutions to senior stakeholders clearly and effectively.
Mentor peers and set best practices in AI engineering and design standards.
Uphold compliance and security standards, addressing key AI risks.
Full Job Description
The E.W. Scripps Company is seeking a Lead AI Architect to design and implement enterprise-scale AI solutions with a strong engineering foundation, bringing deep technical expertise in agentic systems, developer tooling, and AI platform integration. Contribute to the architectural strategy for AI-powered applications - including agent frameworks, tool ecosystems, and model provider integrations - and translate business requirements into production-grade systems built to last.
WHAT YOU'LL DO:
Define and own AI solution architectures spanning agentic workflows, MCP server infrastructure, and multi-model orchestration.
Lead the design and deployment of complex RAG architectures and vector database strategies to support enterprise data retrieval.
Architect and implement MCP (Model Context Protocol) servers and tool integrations, emphasizing advanced function calling and structured output schemas.
Drive integration with major model providers (AWS Bedrock, Azure OpenAI, Anthropic, etc.) and define standards for model selection, routing, and fallback.
Establish engineering patterns for AI observability, prompt engineering, and rigorous AI evaluation frameworks to ensure reliability.
Collaborate with engineering, product, and platform teams to deliver integrated AI solutions within existing software ecosystems.
Ensure scalability, security, and maintainability through sound software architecture principles.
Guide prototyping and proof-of-concept efforts; define technical evaluation criteria for emerging AI tools and frameworks.
Present technical solutions and recommendations to senior stakeholders in a clear and compelling way.
Mentor peers and establish best practices in AI engineering, agent design, and tooling.
Uphold and define security, compliance, and responsible AI standards, specifically addressing OWASP LLM Top 10 risks, prompt injection, and PII leakage.
WHAT YOU'LL NEED:
BS/BA in Computer in related discipline or equivalent years of experience preferred
Generally, 8+ years of experience in related field preferred
Experience in AI/agent development strongly preferred
Relevant certifications (AWS, Azure, cloud AI services) a plus
Experience in the Media and Broadcast domain is a plus
WHAT YOU'LL BRING:
Advanced experience building and deploying production AI applications with a software engineering mindset.
Hands-on expertise designing and deploying AI agents using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
Deep understanding of RAG implementation patterns and vector database optimization.
Proficiency setting up and managing MCP servers and defining tool schemas for LLM tool-use integrations.
Strong experience integrating with cloud-hosted model providers - particularly AWS Bedrock and Azure AI Service.
Proficiency in at least one backend programming language (Python, TypeScript/Node.js, Java, etc.) for building production AI systems.
Familiarity with REST/GraphQL APIs, event-driven architectures, and containerized deployments (Docker, Kubernetes).
Experience with CI/CD pipelines, infrastructure-as-code, and developer tooling in cloud environments.
Strong communication skills with the ability to explain technical concepts to diverse audiences.
Understanding of AI safety, prompt security, and responsible AI practices.
Awareness or experience with LLM fine-tuning is a plus.
#LI-SM2 #LI-Onsite
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