• 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

• Lead the development and implementation of advanced algorithms for robotic perception systems tailored to industrial welding tasks, integrating data from diverse vision sensors such as RGB/GigE, LiDAR, and ToF depth sensors. • Oversee research initiatives to address complex welding-related challenges, utilizing image processing, point cloud data, and 3D sensor fusion, contributing to innovative solutions for domain-specific problems. • Collaborate with multidisciplinary teams to design and lead experiments evaluating state-of-the-art deep learning models, optimizing machine learning systems for robotic perception in welding. • Stay at the forefront of advancements in Robotics, Computer Vision, and ML research, driving the integration of cutting-edge technologies into real-world applications, and ensuring these innovations have a high impact on production systems. • Mentor and guide junior engineers, providing technical leadership and fostering collaboration to enhance team expertise in perception systems and machine learning. • Contribute to strategic decisions about system architecture and the direction of robotics perception technologies within the company, ensuring alignment with product and business goals.