Lead adoption of AI-native engineering practices in SDLC, including Responsible AI controls for AI-generated design/code artifacts and workflow automation. Influence strategic prioritization with data-driven recommendations, KPI frameworks, and experimentation-informed decisions. Define and own end-to-end architecture for software and data platforms supporting engineering health, supportability, customer escalation, and operational intelligence. Lead architecture and design reviews for complex, distributed services and data systems, including tradeoff decisions across scalability, resiliency, performance, security, privacy, and cost. Establish architecture patterns, standards, and guardrails for code quality, testing strategy, observability, deployment safety, and dependency management across teams. Architect and govern data foundations including data models, schemas, lineage, contracts, semantic layers, quality controls, and governance mechanisms. Drive telemetry-first engineering: instrumentation, monitoring, anomaly detection, root-cause workflows, and incident prevention practices. Lead live-site reliability strategy, including Direct Responsible Individual (DRI) readiness, service health indicators, failure mode analysis, postmortems, and continuous reliability improvements. Partner with product management, engineering, support/escalation, and leadership stakeholders to translate ambiguous business needs into clear architecture roadmaps and milestones. Lead cross-team dependency planning and execution; negotiate ownership boundaries and align partner teams to shared delivery outcomes. Ensure compliance and auditability by embedding secure-by-design, least-privilege, data handling controls, and regulatory requirements into architecture and operations. Mentor senior engineers and technical leads; raise architectural quality and decision velocity through clear technical direction and reusable patterns. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 10+ years of technical engineering experience, including 5+ years owning architecture for complex, distributed enterprise systems and data platforms. Experience architecting and delivering large, business-critical initiatives from design through production operations. Hands-on backend engineering expertise, including coding, testing, debugging, reliability, performance, and maintainability. Experience building enterprise AI platforms, including AI harnesses, sandbox environments, and reusable capabilities using frameworks (e.g. AutoGen, Semantic Kernel, LangGraph, etc.) Experience developing production-grade agentic AI systems that reason over data, operate across distributed systems, and generate automated insights. Proven track record leading end-to-end architecture for at least two large, business-critical initiatives spanning design, build, deployment, and operations. Deep hands-on expertise in software engineering fundamentals (coding, design, testing, debugging, reliability, and maintainability). Deep data engineering experience, including Python/PySpark, ETL/ELT pipelines, data modeling, quality, governance, lineage, and large-scale analytics platforms. Expertise in security, privacy, compliance, least-privilege controls, auditability, and secure deployment within enterprise environments. Demonstrated ability to align teams, influence technical roadmaps, establish reusable architecture standards, and mentor senior engineers.