• Lead the technical vision and architecture decisions for our GenAI products, ensuring scalability and performance of the AI-powered location intelligence system. • Establish technical roadmaps aligned with business objectives and scalability requirements. • Design and implement end-to-end AI systems from prototype to production, including LLM integration, agentic architectures, and RAG implementations. • Build robust data pipelines and infrastructure supporting AI/ML workloads at scale. • Oversee model deployment, fine-tuning, and optimization for production environments. • Architect scalable microservices and cloud-native solutions supporting real-time AI applications. • Ensure responsible AI practices including guardrails, performance monitoring, and ethical considerations.

• Building and leading high-performing engineering teams • Delivering secure, scalable enterprise applications • Establishing modern DevOps, CI/CD, QA, and SDLC practices • Supporting the evolution of a unified enterprise platform accessible to both internal users and external clients • Oversee incident response processes and platform monitoring standards • Oversee execution across multiple concurrent software initiatives • Improve backlog management, sprint discipline, and cross-team coordination • Establish developer performance metrics such as: Delivery predictability, Deployment frequency, Change failure rate, System reliability, and uptime • Remove obstacles impacting delivery and execution • Partner with Product and Project leadership to align capacity with priorities • Provide architectural oversight across the organization’s core applications • Guide consolidation of systems into a unified enterprise ecosystem • Define shared services strategy (authentication, authorization, APIs, logging, monitoring, data services) • Support scalable, secure system design capable of supporting external client access • Govern major technical decisions while empowering teams to execute • Oversee development of systems accessible to external clients • Implement role-based access control and identity management standards • Partner with infrastructure and security teams on: Cloud architecture, Secure data handling, Audit logging, Environment separation • Implementing and standardizing CI/CD pipelines • Governing source control and branching strategies • Establishing environment promotion models • Formalizing release management practices • Driving infrastructure-as-code and automation where appropriate • Improving deployment reliability and release velocity • Establish standardized SDLC lifecycle gates • Define testing expectations (unit, integration, regression, security, performance) • Introduce automated testing standards • Improve defect management and root cause analysis • Manage technical debt prioritization • Align development documentation with audit-readiness and compliance needs • Establishing KPIs across: Platform adoption and utilization, System uptime and reliability, Deployment frequency and change failure rate, Security posture and vulnerability remediation timelines, Development throughput and delivery predictability • Developing dashboards and reporting mechanisms that provide visibility to executive leadership • Delivering quarterly updates summarizing: Platform performance and reliability metrics, Roadmap progress, Technical risk areas, Strategic outcomes and value delivered • Evaluating emerging technologies, architectural trends, and evolving client needs to guide platform evolution • Using data to inform investment decisions, staffing models, and technical prioritization

• Managing the development and optimization of software architecture and frameworks for real-world performance while matching or exceeding customer requirements • Working on areas such as component abstraction layers, inter-process data sharing and communication, and process scheduling • Developing efficient mechanisms to maximize utilization on computers with multiple heterogeneous hardware engines • Solidifying existing frameworks and software components and validate with large amounts of real and synthetic the improvements of the computational performance and system utilization • Performing in-vehicle tests, collecting data and completing autonomous drive missions • Developing unit tests, documentation for features, evaluating quality and proposing corrective actions • Developing highly efficient product code in C++, making use of high algorithmic parallelism offered by GPGPU programming (CUDA) • Follow quality and safety standards such as defined by MISRA.

• Build, scale, and grow a team of outstanding engineers and data scientists • Build strong relationships with Data Scientists and Engineers across the business • Act as an ambassador for experimentation across the company