• Define and drive Upstart’s application security strategy, aligning secure-by-design principles with business priorities, regulatory expectations, and our AI-driven product roadmap. • Lead cross-functional security architecture reviews for critical initiatives, influencing engineering, platform, data, and infrastructure decisions to reduce systemic risk early in the SDLC. • Establish and scale a robust threat modeling program for high-risk systems, including customer-facing applications, lending workflows, and ML/AI pipelines, translating findings into durable engineering standards and controls. • Design and standardize application security guardrails across the SDLC, including secure coding practices, automated testing (SAST/DAST/SCA), CI/CD protections, and secrets management. • Partner with Infrastructure and Cloud teams to strengthen the security posture of cloud-native and distributed systems, ensuring defense-in-depth across application and infrastructure layers. • Identify and reduce internal and external attack surface through automation and platform-level solutions, prioritizing long-term risk reduction over point-in-time remediation. • Serve as a senior technical authority during high-severity incidents, driving root cause analysis and durable architectural improvements. • Elevate security maturity across the organization by mentoring engineers, influencing leadership through clear risk metrics, and fostering a culture where security enables innovation.

• Responsible for application layer, Implementation of Service Requests. • Monitoring of server performances and usage of data space

- Provide pre-sales and post-sales technical support to data center customers and partners. - Collaborate with sales teams to identify customer requirements and translate them into technical solutions. - Develop and optimize system architectures for integrating Skeleton’s ultracapacitors into data center UPS, backup power, and energy efficiency applications. - Conduct on-site visits, technical trainings, and product demonstrations. - Support system design, including electrical schematics, thermal considerations, and lifecycle cost analysis. - Work closely with R&D and Product Development to communicate field feedback and improve product-market fit. - Prepare technical documentation including specifications, integration guides, and white papers tailored for the U.S. data center market. - Ensure compliance with relevant U.S. industry standards and certifications (UL, IEEE, etc.).

• Design and develop AI-powered applications, integrating machine learning and generative models into enterprise-grade software products and internal tools. • Own the full software development lifecycle (SDLC), including unit, integration, and end-to-end testing. • Frontend: Develop modern, intuitive interfaces for AI applications (React/Next.js, TypeScript, or equivalent) with a strong focus on usability, accessibility, and AI explainability. • Backend: Implement scalable and secure back-end services (FastAPI, Flask, or Node.js) to expose AI capabilities (LLMs, RAG pipelines, AI agents) through standardized APIs. • Translate data science prototypes and GenAI models (LLMs, diffusion models, transformers) into scalable applications or services with intuitive user interfaces and reliable back-end infrastructure. • Collaborate with insight leaders and business stakeholders on requirements gathering, project documentation, and development planning. • Partners with MLOps and GenAIOps teams to deploy, monitor, and continuously improve AI applications within standardized CI/CD pipelines. • Design and implement integrations using REST, GraphQL, and gRPC; work with cloud-based AI APIs (Azure, AWS, GCP) and enterprise data sources. • Integrate cloud-native AI services (AWS Bedrock, Azure OpenAI) and open-source frameworks (LangChain, LangGraph) into enterprise environments. • Monitor application performance and user adoption, iterating on models and workflows to enhance usability and business impact. • Optimize application performance, infrastructure efficiency, and LLM utilization. • Document architectures, APIs, and deployment processes to ensure transparency, reusability, and maintainability.