• Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services. • Real-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults. • Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains. • End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.

• Designs and Implements complex, mission-critical transformational enterprise grade technical solutions for clients. • Develops high quality deliverables and presentations for Client engineering teams and senior leaders. • Creates and presents solution designs, options, ideas, and innovations to clients’ senior leadership teams. • Develops and Maintains Technical Roadmaps and Innovation Plans. • Develops and Implements CI/CD solutions. • Establishes project estimates and plans, identifies risks and mitigations, oversees development and delivery of technical solutions. • Mentor and support project teammates • Actively lead collaboration within our technical communities • Up to 25% travel

• Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock. • Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. • Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments. • Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production. • Drive best practices for observability, including monitoring, alerting, and logging for AI platforms. • Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types. • Stay current with new tools and technologies to recommend improvements to architecture and operations. • Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).

• Design, deploy, and operate Kubernetes clusters (EKS or self-managed) on AWS, ensuring high availability and security • Build and maintain CI/CD pipelines and internal developer tooling to improve engineering velocity • Automate infrastructure provisioning and operational tasks using Python and tools like Terraform, OpenTofu, and Ansible • Define and enforce platform standards around observability, cost management, and incident response • Partner with application teams to support containerized workloads and resolve infrastructure bottlenecks • Collaborate with Customer Success teams by providing reliable and scalable tooling that supports seamless customer onboarding, integrations, and service delivery