Lead AI Security Automation Engineer
Team: Cyber Data & Analytics (CyberDNA)
Location: Hybrid or Remote, depending on location
Experience: 10+ years in Software Engineering; 4+ years in AI/ML/GenAI
Position Overview
Cyber Data & Analytics (CyberDNA) team is seeking a Lead AI Security Automation Engineer to lead the development of next-generation AI-powered applications, intelligent agents, and automation platforms supporting enterprise cybersecurity operations.
This is primarily a hands-on application software engineering role for someone with strong AI/ML expertise. The ideal candidate will have experience designing, building, and deploying production-grade applications that embed Generative AI, Large Language Models (LLMs), agentic AI, and automation into business workflows to improve efficiency, scalability, and decision-making.
The successful candidate will lead a team of AI engineers while remaining technically hands-on with application architecture, software development, AI integrations, cloud-native platforms, and production deployment.
Important: We are not looking for candidates whose primary experience is assessing AI security risks, conducting AI security reviews, or establishing AI security standards and policies. Security knowledge is a plus, but the priority is strong application development and the ability to build AI-powered solutions.
Key Responsibilities
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Lead, mentor, and develop a high-performing team of AI and application software engineers.
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Architect, develop, and deploy production-grade Generative AI applications, LLM-powered services, intelligent agents, and agentic workflows.
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Embed AI capabilities into existing applications and business processes to improve operational efficiency and automation.
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Design AI agent architectures, tool integrations, memory patterns, guardrails, orchestration, and workflow automation.
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Develop Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, semantic search, context engineering, and knowledge grounding.
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Build scalable cloud-native applications and APIs using AWS, Azure, or Google Cloud Platform, leveraging containers, serverless technologies, and event-driven architectures.
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Develop distributed systems and asynchronous processing solutions using messaging platforms and scalable data stores.
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Implement AI evaluation, testing, monitoring, observability, and regression testing frameworks to ensure application quality and reliability.
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Optimize AI applications for latency, throughput, scalability, context efficiency, and cost.
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Integrate AI applications with enterprise platforms, APIs, data sources, and operational tools.
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Use enterprise-approved AI coding assistants to accelerate development, refactoring, testing, and documentation while validating generated outputs.
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Establish software engineering best practices for application reliability, testing, deployment, and maintainability.
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Drive architectural decisions, conduct code reviews, and collaborate with cybersecurity, infrastructure, data engineering, and business stakeholders.
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Lead complex technical initiatives while maintaining a strong hands-on development contribution.
Required Qualifications
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10+ years of professional software engineering experience.
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4+ years of hands-on experience developing AI, Machine Learning, or Generative AI solutions.
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Proven experience building and deploying enterprise-grade applications and production software platforms.
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Strong programming skills in one or more of Python, JavaScript/TypeScript, Go, Rust, or Java, with Python preferred for AI development.
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Hands-on experience building LLM-powered applications, intelligent agents, and agentic AI workflows.
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Experience with AI orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen.
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Strong experience with RAG, embeddings, semantic search, vector databases, prompt engineering, and context management.
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Strong application architecture, API development, integration, and distributed systems experience.
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Experience with public cloud platforms such as AWS, Azure, or Google Cloud Platform.
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Familiarity with containers, serverless computing, asynchronous processing, messaging, and event-driven systems.
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Experience implementing automated testing, CI/CD pipelines, application monitoring, and production observability.
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Hands-on technical leadership, including mentoring engineers and driving architecture and engineering decisions.
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Experience using enterprise-approved AI-assisted development tools to improve software delivery and engineering productivity.
