• Design, implement and deliver complex, cross-cutting features and systems in React and React Native, and occasionally in Ruby on Rails • Lead efforts to address technical debt and system reliability • Influence and evolve the architecture and system design • Set and uphold high standards for code quality, testing coverage, and documentation • Drive improvements to internal tooling to enhance developer experience and system resilience • Facilitate technical discussions and decision-making • Act as a point of escalation for complex technical challenges • Partner closely with product managers, designers, and engineers

• Lead the architecture and implementation of MLOps/LLMOps systems within OpenShift AI, establishing best practices for scalability, reliability, and maintainability while actively contributing to relevant open source communities • Design and develop robust, production-grade features focused on AI trustworthiness, including model monitoring, bias detection, and explainability frameworks that integrate seamlessly with OpenShift AI • Drive technical decision-making around system architecture, technology selection, and implementation strategies for key MLOps components, with a focus on open source technologies like KServe and TrustyAI • Define and implement technical standards for model deployment, monitoring, and validation pipelines, while mentoring team members on MLOps best practices and engineering excellence • Collaborate with product management to translate customer requirements into technical specifications, architect solutions that address scalability and performance challenges, and provide technical leadership in customer-facing discussions • Lead code reviews, architectural reviews, and technical documentation efforts to ensure high code quality and maintainable systems across distributed engineering teams • Identify and resolve complex technical challenges in production environments, particularly around model serving, scaling, and reliability in enterprise Kubernetes deployments • Partner with cross-functional teams to establish technical roadmaps, evaluate build-vs-buy decisions, and ensure alignment between engineering capabilities and product vision • Provide technical mentorship to team members, including code review feedback, architecture guidance, and career development support while fostering a culture of engineering excellence • Responsible for the safe, auditable, and reliable release of Kubernetes-native AI platform components, with strong emphasis on progressive delivery, operational resilience, and supply-chain integrity.

• As an AI Software Engineering Intern at Praxent, you will be a vital part of our mission to integrate AI seamlessly into legacy software modernization projects, our client’s platforms, and our overall agentic SDLC. • Your role involves collaborating closely with cross-functional teams, including engineering, product management, and client success, to understand the unique challenges faced by our clients and translate these insights into innovative proofs of concept and AI development tools. • You will embrace a hands-on approach to experimentation and iteration, contributing to the creation of actionable AI strategies that align with our clients' business objectives. • By developing agentic AI tools such as prompts, templates, commands, and hooks/skills, you will help streamline the integration of AI technologies, driving efficiency and enhancing the quality of deliverables. • As you engage in iterative processes, your commitment to quality oversight and attention to detail will ensure that our projects maintain high standards of completeness and consistency. • You will also play a role in fostering a culture of continuous learning and improvement within the team, actively participating in knowledge-sharing initiatives to elevate our collective expertise in AI advancements.

• Build, maintain, and evolve backend services and APIs that support advertising data platforms, content and ad categorization, and AI-powered initiatives. • Lead development on front-end products owned by the squad, including tools for moderation, review, and advertiser-facing workflows. • Build and support an annotation application that allows moderators to flag sensitive podcast content, enabling trust and safety at scale. • Deliver UX optimizations that improve ad and content review efficiency and effectiveness. • Develop interfaces that allow advertisers and internal users to interact with AI-powered creative and categorization systems. • Collaborate with engineering squads across Spotify to provide high-quality data solutions, shared knowledge, and best practices.