AI/ML Security Engineer

GuidePoint Security provides trusted cybersecurity expertise and solutions to help organizations minimize risk. They are seeking an AI/ML Security Engineer to design and deploy agentic systems, evaluate AI tools, and lead enablement programs for regional teams.

Responsibilities:

• Design, build, and deploy agentic harnesses and LLM-integrated workflows from prototype through production within existing platforms
• Define and own performance benchmarks to evaluate reliability, accuracy, and continuous improvement of agentic pipelines
• Evaluate and recommend emerging AI tools, models, and platforms for adoption
• Identify, develop, and maintain AI-powered automations and tools that improve efficiency across service delivery, operations, and sales functions throughout the Northeast region
• Lead workflow assessments to identify opportunities for agentic automation and design repeatable, scalable delivery frameworks
• Own documentation, testing, and iteration on productized AI components and proof-of-concept builds for internal and client audiences
• Design and deliver enablement programs that build regional teams' capability to independently operate, maintain, and extend agentic harnesses
• Develop training materials and lead workshops that build agentic AI literacy across functional teams, including handoff documentation and runbooks
• Other duties as assigned

Requirements:

• S. degree in computer science or relevant field and 3 years of experience or M.S degree
• 3+ years of software development or AI/ML engineering experience
• Demonstrated ability to design, author, and deploy agentic harnesses within existing agentic platforms
• Working knowledge of Python and at least one additional language relevant to agentic development
• Working understanding of large language models, prompt engineering, and API-based AI integration
• Ability to independently scope, build, and ship agentic solutions at a mid-level engineering standard, ensuring secure development practices throughout
• Demonstrated ability to explain technical concepts to non-technical audiences
• Experience with cloud platforms (AWS, Azure, or Google Cloud), particularly AI/ML services
• Experience with vector databases, orchestration layers, or workflow automation platforms
• Experience with retrieval-augmented generation (RAG) patterns or tool-use/function-calling with LLMs
• Experience designing and applying evaluation frameworks and benchmarks for AI systems
• Experience designing and facilitating technical enablement programs for mixed audiences