Artificial Intelligence Engineer

AI Developer #2614Position Summary:Our partner is a U.S.-based international bank advancing core financial operations through secure, enterprise-grade artificial intelligence within a highly regulated environment. As part of this evolution, they are adding a Senior AI Developer to architect and deploy production-grade AI systems that replace legacy workflows and enhance operational efficiency. In this role, you would design AI agent systems using Gemini Enterprise, integrate them into core banking platforms, and build predictive models supporting fraud detection, AML investigations, loan processing, and reporting. This is hands-on architecture with real enterprise impact, requiring solutions that are innovative, auditable, and compliant.Experience and Education: BS in Computer Science, Information Technology, Software Engineering, or equivalent experience/fieldBackground building production-grade AI or ML systems in enterprise environmentsHands-on work deploying solutions within Google Cloud PlatformPrior exposure to AML, fraud review, or compliance-driven systemsProven ability to design system architecture rather than only contributing codeDemonstrated capability in integrating AI systems with APIs and backend servicesBackground in financial services, fintech, fraud detection, risk modeling, or regulated healthcareSkills and Strengths: Gemini EnterpriseGoogle Cloud PlatformVertex AIAI Agent ArchitectureRetrieval Augmented GenerationMulti-Agent OrchestrationTool CallingAPI IntegrationBigQuery MLPredictive ModelingRegression ModelingTime Series ForecastingFeature EngineeringModel EvaluationMLOpsExplainable AIModel MonitoringRisk ModelingBackend EngineeringEnterprise ArchitecturePrimary Job Responsibilities: Design and deploy AI agents within Gemini EnterpriseArchitect end-to-end AI systems integrated with core banking platformsImplement Retrieval Augmented Generation frameworksDesign multi-agent orchestration workflowsBuild predictive models for fraud detection and AML processesDevelop time-series forecasting models to support financial decision-makingEvaluate latency versus accuracy tradeoffs in model deploymentEstablish governance guardrails and compliance controls within AI systemsImplement model monitoring, drift detection, and performance benchmarkingCollaborate with risk, compliance, and business stakeholdersEvaluate and recommend AI tools and architectures within the existing Google Cloud ecosystem, ensuring alignment with business and compliance objectivesIntegrate AI agents with APIs, databases, and core banking systemsCommunicate technical decisions clearly to executive leadershipReplace legacy manual workflows with secure AI-driven automationEnsure auditability and explainability across all AI implementations