Lead Applied AI Engineer

Location: New York, NY / Louisville, KY (Remote)

Job Description: We are seeking an accomplished Lead Applied AI Engineer to architect and deliver advanced AI systems that seamlessly integrate Generative AI capabilities, AI agents, and modern enterprise platforms. This role is responsible for designing, building, deploying, and scaling production-grade AI solutions that support large-scale business operations while maintaining high standards of security, reliability, governance, and responsible AI practices. The Lead Applied AI Engineer will define technical standards, lead enterprise AI adoption, establish engineering best practices, and mentor engineering teams. This position operates at the intersection of AI innovation, enterprise architecture, platform engineering, and responsible AI governance. Key ResponsibilitiesAI Solution Architecture

• Architect comprehensive end-to-end AI systems including:o Advanced RAG (Retrieval-Augmented Generation) pipelineso Multi-stage retrieval and re-ranking architectureso Agent orchestration frameworks coordinating multiple specialized agentso Multi-model AI integrations leveraging model-specific strengths
• Design solutions with modularity, extensibility, scalability, and operational excellence to support evolving business requirements. AI Engineering Standards & Optimization
• Define enterprise standards for:o Prompt engineeringo Prompt templates and versioningo Testing methodologieso Evaluation frameworks
• Establish performance optimization strategies covering:o Model selection criteriao Caching patternso Resource utilizationo Cost optimization Production Deployment & Reliability
• Lead deployment of AI solutions into production environments with:o Comprehensive observabilityo Logging and tracingo Reliability engineering practiceso Graceful degradation mechanismso Circuit breaker implementationo Real-time monitoring dashboardso Automated alertingo Incident response procedures
• Ensure AI services meet stringent service-level objectives and enterprise reliability expectations. Data & Retrieval Architecture
• Design scalable data ingestion frameworks that process:o Structured data sourceso Unstructured documentso Real-time event streams
• Develop:o Vector database architectureso Hybrid search capabilitieso Data preprocessing pipelineso Data quality monitoring frameworks
• Ensure high-quality inputs for AI systems through cleansing, enrichment, and governance processes. AI Evaluation & Continuous Improvement Establish quantitative evaluation frameworks for AI systems. Implement:o A/B testing capabilitieso Performance benchmarkingo User feedback analysiso Telemetry-based optimization
• Drive continuous improvements across:o Promptso Retrieval strategieso Agent workflowso Model configurations Platform & Infrastructure Collaboration
• Partner with platform and infrastructure teams to ensure readiness for AI workloads, including:o GPU infrastructureo Model serving platformso Feature storeso Scalable data storageo Networking infrastructure
• Define requirements for enterprise AI platform capabilities and integration patterns.