• Architect the Hybrid Engine: Design and build the "Project Bridge" architecture, transitioning the platform from heuristic-based logic (Cohort/Geo-based) to fully real-time ML personalization (Vector Search/Neural Networks). • Real-Time Inference at Scale: Steer the design and deployment of low-latency services (Segment Service & User Profile Service) using Redis/DynamoDB to serve personalized board orderings, deposit defaults, and "For You" feeds in milliseconds. • Feature Engineering & Data Strategy: Partner with Data Science to build the logging pipelines that tag why a user saw an item (data labeling). You will create the feature store required to train future neural networks for individual-level personalization. • Solve the "Cold Start" Problem: Implement logic for dynamic league ordering and deposit smart-defaults based on geospatial data and initial user cohorts, ensuring immediate relevance for new users.

• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance. • Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them. • Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system. • Writes code with testability, readability, edge cases, and errors in mind. • Train models on well-defined datasets to improve model efficacy on specialized attacks • Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling. • Analyze FN and FP datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy. • Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises

• Design the comprehensive, 5–10-year architectural vision for a unified ML Ops platform that strategically leverages both AWS (SageMaker, EKS) and Azure (Azure ML, AKS) services to maximize resilience and capability. • Establish and lead the ML/AI Architecture Review Board (ARB), setting global standards for technology stack selection, architectural patterns, and security guardrails for all AI production deployments. • Direct the enterprise-wide adoption and governance of IaC using Terraform or equivalent tools to ensure consistent, auditable, and secure provisioning of multi-cloud infrastructure (compute, networking, security groups, data plane). • Architect and oversee the implementation of automated, end-to-end Continuous Integration, Continuous Delivery, and Continuous Training pipelines that facilitate rapid, zero-downtime model deployments and rollbacks across hybrid/multi-cloud environments. • Design the architecture for containerized ML workloads and inference services using enterprise-scale Kubernetes (AKS/EKS) clusters, focusing on service mesh implementation, efficient autoscaling strategies, and network isolation. • Ensure the ML platform architecture can handle the massive scale and high throughput required for real-time risk, fraud, and customer interaction models within financial services. • Architect and enforce robust Model Risk Management (MRM) frameworks, embedding regulatory compliance, audit trails, model versioning, and explainability (XAI) requirements directly into the ML Ops pipelines to meet banking/insurance sector mandates. • Define the enterprise standard for AI Ops observability, leveraging unified monitoring tools (e.g., Prometheus/Grafana) to track multi-cloud system health, proactively detect and auto-remediate Model Drift, Data Quality issues, and prediction latency. • Implement strategic architectural patterns and governance policies to drive maximum cost-efficiency and transparency across all Azure and AWS ML/compute resources, including chargeback and budget enforcement. • Design and mandate secure data governance, Role-Based Access Control (RBAC), and Secrets Management across the multi-cloud architecture, ensuring data isolation and secure cross-cloud communication.

• Design, deploy, and maintain Kubernetes infrastructure supporting AI/ML workloads • Manage containerized services, autoscaling, networking, and resource optimization • Design and build high-performance Python APIs and services using FastAPI or similar frameworks • Architect backend systems for scalability, reliability, and low latency • Build integrations between AI/ML systems and the broader Albert platform • Build and operate distributed systems that handle compute-intensive and high-throughput workloads • Design for fault tolerance, graceful degradation, and horizontal scalability • Implement async workflows, job queues, and task orchestration as needed • Architect and maintain data pipelines and storage systems supporting AI/ML workflows • Implement observability including logging, metrics, tracing, and alerting • Own system reliability—troubleshoot issues, conduct post-mortems, and continuously improve • Design CI/CD pipelines and promote automation best practices • Partner closely with ML engineers to understand requirements and deliver production-ready infrastructure • Translate ML prototypes and research code into scalable, maintainable systems