Lead AI Security Automation Engineer

About the role

The Lead AI Security Automation Engineer will 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.

Responsibilities

• 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.

Qualifications

• 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.
• 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.
• Strong 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.
• Proven experience in Python, JavaScript/TypeScript, Rust, Go (Golang), Bash, and PowerShell.
• 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.