• 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.

• Own the technical architecture of the data platform end-to-end: ingestion, storage, transformation, orchestration, serving, and observability layers. • Author and maintain the platform architectural vision document; lead quarterly architecture reviews to assess alignment with organizational goals and technology trends. • Define and evolve the target-state architecture for the platform, establishing a multi-year technology roadmap in partnership with engineering leadership. • Evaluate emerging technologies, frameworks, and make evidence-based adoption recommendations. • Serve as the final technical escalation point for complex design questions, cross-team conflicts, and build-vs-buy decisions affecting the platform. • Lead the planning, execution, and delivery of multi-quarter platform initiatives involving multiple engineers, cross-functional dependencies, and significant organizational impact. • Break down large, ambiguous programs into scoped workstreams; assign technical leads per workstream, define milestones, and manage cross-team dependencies and risk. • Drive initiative kick-offs with clear problem framing, success criteria, architectural constraints, and delivery phasing — from 0-to-1 exploration through production hardening. • Maintain stakeholder alignment throughout delivery: proactively communicate status, surface trade-offs, and escalate blockers to leadership before they become risks. • Lead post-mortems and retrospectives for large initiatives; document and socialize lessons learned to raise the organizational bar on delivery excellence. • Exercise functional oversight across the EDAP team: review technical designs, set quality gates, approve architectural decisions, and ensure consistency of implementation patterns. • Define, document, and socialize platform engineering standards including coding conventions, testing requirements, CI/CD practices, schema design guidelines, and SLA frameworks. • Establish and own the platform's technical review process (design review): triage incoming projects, chair review sessions, and ensure decisions are documented and traceable. • Identify and drive resolution of technical debt, redundancy, and architectural drift that impede platform reliability, developer productivity, or scalability. • Partner with security, compliance, and infrastructure teams to ensure platform systems meet governance, data privacy, and regulatory requirements by design. • Serve as a primary technical mentor for junior and senior data platform engineers across the platform; provide structured coaching on system design, technical communication, and engineering judgment. • Conduct and lead design reviews, architecture critiques, and technical deep-dives that strengthen the overall capability of the engineering team. • Define what technical excellence looks like at each level of engineers and participate in calibration discussions. • Represent the data platform in engineering-wide forums, all-hands, and external venues (conferences, open-source communities, recruiting events). • Build a culture of documentation, reliability, and platform-as-a-product thinking across all data platform engineering functions. • Participate in goal planning cycles as a technical voice; define engineering-led goals that improve platform reliability, developer experience, and data quality.

• You build out the cloud and infrastructure for a production-grade platform • You introduce new cloud components for persistence, monitoring, and security • You establish a resilient operations foundation with logging, observability, and CI/CD pipelines • You provision cloud resources consistently and maintainably using Infrastructure as Code • You prepare technical foundations for robust artifact management and controlled audit access • You make well-founded technical decisions and surface risks early • You drive larger topics pragmatically into viable implementations

• Iteratively design and develop scalable, resilient, and efficient platform solutions using cloud services and modern software design frameworks. • Create modular, reusable infrastructure using Terraform and related tools. • Partner interdepartmentally and cross-functionally to strengthen DevOps practices through automation. • Architect, develop, and maintain CI/CD pipelines for platform functions and customer needs at scale. • Deliver solutions based on event-driven, object-oriented, and functional design patterns. • Own the design and implementation of monitoring and logging solutions to ensure reliability and performance. • Support the definition and maintenance of a lifecycle process for platform systems to ensure updates, patches, and security compliance. • Apply deep expertise in platform engineering while leveraging cross-disciplinary knowledge to drive effective collaboration across development, operations, and security teams. • Resolve cloud infrastructure issues quickly, analyze performance, and implement improvements. • Analyze system performance and implement optimizations to improve speed, efficiency, and resource utilization. • Anticipate and plan infrastructure scaling to meet the growing needs of the business. • Apply a security-first approach, ensuring SOC, SOC2, HIPAA, NIST, and regulatory requirements are met. • Implement and manage IAM, firewalls, and SIEM solutions. • Integrate diverse and non-traditional systems to deliver practical, forward-thinking outcomes. • Navigate challenges creatively and find effective solutions when conventional approaches fall short. • Lead assigned projects, driving cross-team collaboration, execution delivery, and accountability. • Write clean, efficient, and well-documented code while following best practices and established development standards. • Take part in the on-call rotation to ensure the reliability and availability of platform services. • Use generative AI tools such as Claude, Copilot, or ChatGPT to improve productivity and deliver high-quality outcomes.