• Provide technical leadership and expertise to help build ad verification, analytics, and anti-ad fraud solutions • Architect, design, build and integrate core ad analytics and anti ad fraud products end to end • Work with kubernetes and jenkins deployment systems AWS • Collaborate closely with product and data teams to embed new capabilities and signal intelligence into applications • Lead the entire software lifecycle — from design and hands-on coding to testing, deployment, and observability

• Own and contribute to architecture and development across products, driving technical excellence in both frontend and backend systems. • Develop and optimize backend API services with test-driven development including advanced caching and messaging for reliability and scalability. • Deliver high-quality frontend experiences using modern frameworks, with strong fundamentals in component-based architectures and state management. • Integrate and support observability, logging, and monitoring to ensure platform reliability and rapid incident response. • Ensure top-tier application security and compliance, applying best practices across frontend, backend, and API layers (authentication, authorization, rate-limiting, OWASP). • Leverage LLMs and AI tools to write, refactor, test, and debug code efficiently, implementing guardrails for reliability. • Participate in design reviews, deep dives, and on-call rotations to support a culture of accountability and operational excellence.

• Develop and maintain full-stack applications, ensuring seamless integration between front-end and back-end systems. • Build responsive, user-friendly interfaces while implementing AI-driven features (e.g., chatbots, recommendation engines, predictive analytics, or automation tools). • Design and optimize APIs and microservices to connect AI/ML models with applications. • Collaborate with AI/ML engineers, data scientists, and product managers to translate business needs into technical solutions. • Deploy and monitor AI models in production environments, ensuring performance, scalability, and reliability. • Manage databases and implement efficient data pipelines for AI features. • Ensure security, compliance, and data privacy in AI-enabled applications. • Stay updated on emerging trends in full-stack development, cloud technologies, and AI/ML advancements.

• Design, build, and evolve resilient, scalable digital products • Maintain application quality • Actively participate in product decisions • Work in a collaborative environment, supporting and being supported by teammates • Ensure solution quality through automated tests • Document work performed • Identify improvements and opportunities in products • Handle uncertainty and embrace change • Provide technical influence across the engineering team • Help reduce complexity in the development process • Work independently and lead end-to-end tasks