• Design and develop serverless and containerized services using Go and Python • Build and operate microservices using cloud-native compute platforms across AWS, Azure, and GCP (e.g., Lambda, Azure Functions, ECS/Fargate, managed container services) • Implement Infrastructure as Code using Terraform to manage multi-account and multi-project cloud environments • Develop and maintain CI/CD pipelines using Jenkins and CloudOS • Build event-driven workflows using cloud-native messaging and orchestration services • Implement scalable data persistence using managed databases and object storage services • Contribute to security and compliance controls • Implement monitoring, logging, and tracing using cloud-native observability tools and custom metrics • Support AI-assisted observability initiatives, including intelligent alerting, anomaly detection, and signal correlation using telemetry data • Optimize services for reliability, performance, and cost across cloud providers • Participate in on-call rotations and production incident response • Build and enhance automated cloud account and project provisioning workflows • Develop self-service APIs for infrastructure requests and governance • Implement cross-account and cross-project identity and access controls • Create reusable Terraform modules and service templates • Build and maintain Docker images and manage deployments via container registries

• Design, build, and maintain backend services and APIs to support our GenAI product using Python and FastAPI, ensuring they are scalable, reliable, and secure. • Develop and operate LLM-powered applications and agentic workflows using LangChain, LangGraph, and LangSmith, including tracing, evaluation, and observability. • Develop and manage infrastructure using Terraform, applying infrastructure-as-code (IaC) practices across backend and ML resources. • Build and maintain monitoring and alerting systems to surface service and API health issues early, ensuring reliability and performance. • Handle large datasets to ensure data is processed, ready for model context, and collaborate in the platform data pipeline. • Evaluate and select appropriate tools and technologies and define the overall technical strategy. • Assess and interpret data to gain insights, especially for testing and monitoring purposes. • Build and maintain CI/CD pipelines, including infrastructure-as-code (IaC), and automate deployment and monitoring across backend services and ML workflows. • Continuously improve and automate monitoring and alerts processes. • Optimize infrastructure and services for performance, scalability, reliability, and cost efficiency, and troubleshoot issues across deployment and runtime. • Troubleshoot and resolve issues related to pipeline deployment and performance. • Contribute to the development of internal engineering standards and documentation. • Advocate for engineering best practices and promote a culture of continuous improvement also mentoring and guiding others in the company in this topic. • Understand the requirements of stakeholders and translate them into technical solutions. • Develop robust model monitoring and alerting systems to ensure model performance and identify issues. • Support our mission and vision and embody and demonstrate AppOmni core values of Trust, Transparency, Quality, Customer Focus, and Delivery. Comply with all company policies and governing laws and regulations.

• Drive the technical vision and architecture for Aerial Capture, driving clean code standards, setting technical direction, and making key architectural decisions. • Serve as a primary code contributor, lead projects end-to-end, and proactively coordinate work to ensure predictable delivery, technical quality, and the quick unblocking of teammates. • Raise the technical bar across the team by providing hands-on mentorship, pairing, and timely, direct technical feedback to foster engineer growth. • Take ownership of system health, including reliability and maintainability. Champion the reduction of technical debt and measure outcomes to drive value throughput. • Act as the technical voice for the team in cross-functional forums, communicating decisions, trade-offs, and project status clearly to Engineering Managers and stakeholders.

• Enhance NVIDIA's future GPU Networking offerings for accelerating AI workloads. • Lead vision, architecture and design of such technologies. • Lead proof-of-concept development to evaluate and drive such technologies. • Identify and evaluate new technologies, innovations and partner relationships for alignment with our technology roadmap and business value. • Work with the community and maintainers to drive strategic technologies.