Senior Security Engineer - cloud security, DevSecOps, application security, and AI security - Dallas TX -Hybrid work

Lead customer solution delivery and a team of Forward Deployed Security Engineers, combining hands-on technical leadership with customer engagement across cloud security, DevSecOps, application security, and AI security. Translate complex security challenges into architectures, implementation roadmaps, and production-ready controls, owning technical direction and delivery outcomes from discovery through validation and operational handover. Guide engineering teams and manage dependencies, risks, and escalations across multiple workstreams to meet customer security and operational requirements., Process Flows -Mentor and Knowledge transfer to client project team members -Participate as primary, co and/or contributing author on any and all project deliverables associated with their assigned areas of responsibility -Participate in data conversion and data maintenance -Provide best practice and industry specific solutions -Advise on and provide alternative (out of the box) solutions -Provide thought leadership as well as hands on technical configuration/development as needed. -Participate as a team member of the functional team -Perform other duties as assigned.

• Proven experience leading large engineering teams, reviewing technical work, mentoring engineers, and delivering complex security programs across multiple workstreams.
• Strong customer-facing skills in technical discovery, solution development, stakeholder management, and executive communication; communicate risks, tradeoffs, priorities, and progress to security leaders, architects, and engineering teams.
• Lead threat modeling, architecture reviews, and security assessments across applications, APIs, cloud platforms, and AI systems; balance security, delivery timelines, performance, and customer requirements.

Technical skills and systems experience:

• Deep expertise in cloud security, identity, workload protection, application security, containers, infrastructure as code, and CI/CD; define DevSecOps strategies for automated testing, pipeline security gates, software supply chain controls, and remediation workflows.
• Expertise in LLM, RAG, vector database, AI agent, tool-execution, model deployment, model lifecycle, and MLOps architectures, including data flows, trust boundaries, performance, reliability, and cost; guide retrieval architecture, orchestration, integration strategy, and operating models.
• Apply AI-specific threat modeling, security controls, and evaluation methods; define enterprise controls for prompt injection, data leakage, insecure retrieval, excessive agent permissions, unsafe tool execution, testing, and monitoring.

Expected deliverables: Customer-specific security architectures and implementation roadmaps; validated production-ready controls and operational handover; reusable reference architectures, tooling, and playbooks; measurable security outcomes and engineering standards.