Senior AI Security Automation Engineer

Who we are looking for

State Street’s Cyber Data & Analytics (CyberDNA) team is seeking a Senior AI Security Automation Engineer to help shape the next generation of cybersecurity data, analytics, and AI-powered platforms. Partnering closely with Global Cyber Security teams, Infrastructure Teams, and Enterprise Continuity Services, this team develops advanced data platforms, intelligent automation solutions, and engineering capabilities that enable cybersecurity teams to make faster, AI-driven decisions and strengthen the firm’s ability to detect, prevent, and respond to evolving cyber threats.

Why this role is important to us

Through innovation in AI, analytics, and automation, CyberDNA team plays a critical role in protecting State Street, its clients, and its partners from increasingly sophisticated global threat actors.  This role sits at the intersection of Artificial Intelligence, Cybersecurity, Data Engineering, and Full-Stack Software Development, with a primary focus on enabling secure, scalable, and governed AI adoption across the enterprise for supporting Cyber Security functions.  The successful candidate will be part of a global AI security automation engineering team focused on delivering next-generation AI platforms, autonomous agents, security automation solutions, and data-driven applications that strengthen cyber resilience, accelerate innovation, and transform cybersecurity operations through intelligent automation.

What you will be responsible for

As Senior AI Security Automation Engineer, you will

• Drive the architecture and hands-on delivery of scalable, reliable agentic AI platforms for security workflows
• Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration
• Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management
• Engineer cloud-native AI services in AWS, Azure and GCP using containers and serverless patterns, event-driven messaging, and distributed data stores
• Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
• Build well-governed APIs and integrations that connect AI capabilities to security platforms, tools, and business processes
• Establish evaluation, research, regression testing, and observability frameworks to continuously improve quality and agent behavior
• Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle
• Mentor  junior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

What we value

These skills will help you succeed in this role

• Demonstrated experience architecting, developing, and deploying production-grade Generative AI and Large Language Model (LLM) based solutions, including agentic workflows, intelligent agents, and enterprise tool integration frameworks.
• Strong software engineering fundamentals with expertise in designing and delivering cloud-native applications and services leveraging containers, serverless architectures, and modern public cloud platforms.
• Proven experience building highly scalable distributed systems utilizing asynchronous processing, event-driven architectures, durable messaging, and high-performance data access patterns.
• Hands-on expertise developing Retrieval-Augmented Generation (RAG) solutions, including embeddings, semantic search, knowledge grounding, context engineering, prompt optimization, and prompt lifecycle management.
• Experience implementing AI evaluation, testing, monitoring, and observability frameworks to measure model quality, reliability, performance, and safe operation in production environments.
• Strong API …