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As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.\nJob responsibilities:\nDrives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.\nArchitect and implement resilient, highly scalable, fault-tolerant, low-latency services and drive target-state architecture.\nDesign and deploy services that integrate with enterprise systems; ensure functional, performance, scalability, security, governance, and auditability requirements are met.\nLead and mentor the development team in a high-pressured delivery environment; manage multiple deliverables across business groups and strengthen stakeholder relationships.\nCollaborate with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues.\nBuild and mature capabilities that execute ML pipelines for fraud detection and risk assessment; support modeling teams in implementation and tooling.\nProduction Alize models built by data scientists, including validation readiness and quality controls prior to live usage.\nDesign and own reusable ML platform components (e.g., feature-store patterns, delivery pipelines) and establish monitoring/alerting for performance, scalability, availability, and reliability.\nBuild agentic AI services to automate and enhance engineering and model-ops workflows (tool-using agents, orchestration, state management, and audit-ready traceability).\nDefine and implement guardrails and evaluation approaches for agentic AI in production (quality, safety, latency, and cost).\nRequired qualifications, capabilities, and skills:\nFormal training or certification on software engineering concepts and 5+ years applied experience \n10+ years of recent hands-on software development experience in large-scale distributed systems, primarily Python/Java/J2EE and modern Java/Spring Boot microservices.\n3+ years of experience developing in Linux environments.\nDemonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.\nStrong understanding of responsible AI use in engineering 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