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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Web Proxy & AI Security Engineer - Vice President - **Company:** Morgan Stanley - **Location:** Dallas, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, User Authentication, Microsoft Azure, Cloud Computing, Cloud Computing Security, Cyber Security, Computer Programming, Data Governance, Data Security, Digital Architecture, Identity and Access Management, Python (Programming Language), Key Management, OAuth, OpenID, OpenAI, Cloud Services, Akamai, Microsoft Copilot, Zero Trust Network Access, Web Application Security, Single Sign-On, Systems Integration, WebSocket, Data Logging, Data Processing, Enterprise Software Applications, Data Classification, Istio, Large Language Models, Software Security, Apigee, AI Platforms, Kubernetes, Low Latency, Palo Alto Networks, Cloudflare, Fortinet, Machine Learning Operations, Api Gateway, Restful APIs, Grpc, Api Management, Golang - **Published:** October 15, 2026 - **Apply:** https://startup.jobs/lead-web-proxy-ai-security-engineer-vice-president-474-ms-services-group-inc-co-10456219 ## About the Role * Bachelor's degree in Computer Science, Engineering, Cybersecurity, or related field, or equivalent practical experience. * 5+ years of experience in engineering and design of web security and ZTNA platfoms in Netskope, Zscaler, Palo Alto, Fortigate. * Fluency in AI application protocols such as WebSocket, Server Sent Events, gRPC, and experience in troublshooting AI connverastions from OpenAI, Anthropic, Grok, MSFT Copilot, Cowork, for both GenerativeAI and AgenticAI * Hands-on experience with secure web gateways, proxies, CASB, SSE/SASE, WAF, API gateways, or related access-control technologies. * Experience integrating enterprise applications with REST APIs and cloud services. * Strong understanding of authentication and authorization patterns, including SSO, OAuth, OIDC, mTLS, and service-to-service identity. * Experience with DLP, data security controls, logging pipelines, and policy enforcement in enterprise environments. * Strong scripting or programming skills in Python, Go, Java, or similar. * Familiarity with cloud platforms such as AWS, Azure, or GCP. * Ability to troubleshoot across network, security, application, and platform layers. * Strong written and verbal communication skills with the ability to explain technical tradeoffs to security, risk, and business stakeholders. Preferred Qualifications * Experience securing or enabling access to LLMs, GenAI platforms, or model APIs. * Experience with AI gateways, model routing, prompt filtering, token-aware controls, or AI observability tools. * Knowledge of enterprise controls around data residency, privacy, records retention, and regulated data handling. * Experience with tools such as Netskope, Zscaler, Palo Alto, Cloudflare, Akamai, Azure API Management, Kong, Apigee, or similar platforms. * Familiarity with Kubernetes, service mesh, reverse proxies, and zero-trust access patterns. * Experience supporting regulated environments such as financial services, healthcare, or public sector. * Exposure to model risk, AI governance, or third-party technology risk frameworks. Key Skills * Secure web gateway architecture * API security * AI/LLM platform integration * DLP and data governance * Network and application troubleshooting * Cloud security * Identity and access management * Observability and performance engineering ## Description In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Principal Cyber Security Engineering position at the Vice President level, which is part of the job family responsible for providing specialist cyber expertise and creating solutions that protect the organization's systems and networks against actual and potential security threats and vulnerabilities. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What you'll do in the role: * Design and implement secure access patterns for enterprise AI services behind secure web gateways, AI gateways, proxies, and cloud security controls. * Enable access to sanctioned LLMs and AI applications while blocking or restricting unsanctioned services. * Partner with security, infrastructure, legal, risk, and application teams to onboard AI use cases in a controlled manner. * Build traffic-routing, authentication, and policy-enforcement mechanisms for user-to-AI and app-to-AI interactions. * Integrate DLP, data classification, logging, redaction, token filtering, and content inspection into AI traffic flows. * Optimize latency, throughput, reliability, and user experience for AI access paths traversing security gateways. * Develop guardrails for prompt/response inspection, data exfiltration prevention, tenant isolation, and approved use enforcement. * Implement monitoring and alerting for model usage, blocked events, policy violations, response times, error rates, and adoption metrics. * Support evaluation and rollout of technologies such as secure web gateways, CASB, SSE/SASE, AI gateways, API management, and service mesh controls. * Create reusable patterns, reference architectures, and onboarding playbooks for AI application teams. * Perform root-cause analysis for performance bottlenecks, access failures, and policy conflicts affecting AI use cases. * Contribute to standards for AI access, third-party model connectivity, secrets management, and auditability. * Document architecture decisions, operational procedures, and control evidence for internal governance and regulatory review., * Approved AI tools and model endpoints are onboarded quickly and safely. * Latency introduced by gateway and security controls is minimized and measurable. * Unsanctioned AI usage is reduced through effective routing and enforcement. * Sensitive data is protected with strong inspection, redaction, and blocking controls. * Developers and end users can reliably access sanctioned AI services with clear support models. * Architecture, logs, and controls are well documented and auditable.