What are the responsibilities and job description for the Artificial Intelligence Engineer position at Ranger Technical Resources?
AI Developer #2614
Position 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/field
- Background building production-grade AI or ML systems in enterprise environments
- Hands-on work deploying solutions within Google Cloud Platform
- Prior exposure to AML, fraud review, or compliance-driven systems
- Proven ability to design system architecture rather than only contributing code
- Demonstrated capability in integrating AI systems with APIs and backend services
- Background in financial services, fintech, fraud detection, risk modeling, or regulated healthcare
Skills and Strengths:
- Gemini Enterprise
- Google Cloud Platform
- Vertex AI
- AI Agent Architecture
- Retrieval Augmented Generation
- Multi-Agent Orchestration
- Tool Calling
- API Integration
- BigQuery ML
- Predictive Modeling
- Regression Modeling
- Time Series Forecasting
- Feature Engineering
- Model Evaluation
- MLOps
- Explainable AI
- Model Monitoring
- Risk Modeling
- Backend Engineering
- Enterprise Architecture
Primary Job Responsibilities:
- Design and deploy AI agents within Gemini Enterprise
- Architect end-to-end AI systems integrated with core banking platforms
- Implement Retrieval Augmented Generation frameworks
- Design multi-agent orchestration workflows
- Build predictive models for fraud detection and AML processes
- Develop time-series forecasting models to support financial decision-making
- Evaluate latency versus accuracy tradeoffs in model deployment
- Establish governance guardrails and compliance controls within AI systems
- Implement model monitoring, drift detection, and performance benchmarking
- Collaborate with risk, compliance, and business stakeholders
- Evaluate and recommend AI tools and architectures within the existing Google Cloud ecosystem, ensuring alignment with business and compliance objectives
- Integrate AI agents with APIs, databases, and core banking systems
- Communicate technical decisions clearly to executive leadership
- Replace legacy manual workflows with secure AI-driven automation
- Ensure auditability and explainability across all AI implementations