Lead AI Software Security Engineer

Lead AI Security Automation Engineer to 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. As cybersecurity operations increasingly rely on AI, automation, and data-driven decision-making, this role will lead the development of the foundational platforms and capabilities that enable intelligent security operations at enterprise scale. 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 establish and lead a team of AI engineers 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 the Lead AI Security Automation Engineer, you will

• Build, lead, and mentor a high-performing team of AI Automation Engineers focused on advancing cybersecurity operations through automation and AI-driven innovation.
• Lead 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 solutions 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 senior & 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 design and integration experience, including the development of secure, reusable, and scalable platform services that enable enterprise-wide adoption of AI capabilities.
• Demonstrated technical leadership skills with a track record of mentoring engineers, driving architectural decisions, influencing technology strategy, and collaborating effectively with cross-functional stakeholders.
• Hands-on experience utilizing enterprise-approved AI-assisted software development tools to accelerate application delivery, improve code quality, streamline testing, and enhance documentation, while ensuring outputs are validated through secure coding practices, peer review, and automated testing.
• Strong understanding of responsible AI principles, including data privacy, security, governance, resiliency, and risk management, with the ability to guide teams in the safe and effective use of AI technologies.
• Deep understanding of cybersecurity functions to support threat detection engineering, threat hunting, offensive/defensive security, Threat intelligence and SOC operations.
• Strong understanding of cybersecurity operations, including threat detection, incident response, threat hunting, security analytics, and security automation.
• Proven ability to lead and influence geographically distributed teams through virtual collaboration, fostering strong partnerships, driving technical outcomes, and building effective relationships across engineering, security, and business organizations.
• Deep understanding of CI/CD tools (Jenkins, Harness, Spinnaker, Argo CD, etc.) and methodology, production experience in designing and implementing CI/CD pipelines with a focus on helping teams release frequently to production while maintaining deployment reliability
• Strong software development and automation skills with demonstrated experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell.
• Proven ability to lead complex technical initiatives while remaining deeply hands-on.
• LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks.
• Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow.
• Experience with vector databases, semantic search, and enterprise RAG platforms.
• Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks.
• Knowledge of Responsible AI, data governance, and model risk management.

Education & Preferred Qualifications

• Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Cybersecurity, or a related discipline.
• 10+ years of professional software engineering experience.
• 4+ years of experience developing AI, Machine Learning, or Generative AI solutions.
• Demonstrated success designing and deploying enterprise-scale applications and platforms.
• Experience developing cybersecurity, analytics, or operational intelligence solutions.

Work Requirement

• Work shift is 8 AM – 5 PM local time with occasional Level 3 escalation resolution to support operational incidents
• This hybrid role includes an in-office presence requirement of 2–4 days per week, consistent with the organization's hybrid work policy.