Sr. Security Harness Engineer

About the role

Location: Round Rock, TX - Building 1. Work model: Fully onsite. Overtime: Not required; manager approval required.

Design and deliver production-grade AI capabilities that strengthen software security across the SDLC, with a focus on secure agentic workflows, AI-generated code assurance, enterprise integration, and scalable engineering practices.

Everforth Apex is a world-class IT services company that serves thousands of clients across the globe. When you join Everforth Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in many awards, including ClearlyRateds Best of Staffing® in Talent Satisfaction in the United States and Great Place to Work® in the United Kingdom and Mexico.

Responsibilities

• AI-Powered SDLC Engineering: Design, build, and deploy AI-enabled capabilities across the software development lifecycle, including specification-driven development workflows that translate well-formed specifications into secure, verifiable implementations; assurance for AI-generated code through guardrails, policy enforcement, and verification for code produced by AI assistants and agents; developer-assist and verification tooling that automates security checks, including design review, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit support.
• Enterprise Integration: Integrate solutions with source control, CI/CD, ticketing, security scanning, identity, and internal platforms using APIs, webhooks, and protocols such as MCP (Model Context Protocol).
• Architecture & Solution Design: Apply sound architecture and systems design practices, including well-defined service boundaries, appropriate data models, secure defaults, observability, and extensibility.
• Stakeholder Partnership: Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate trade-offs, and support solution adoption.

Requirements

• Demonstrated experience developing and deploying AI-based solutions in production environments with measurable business or operational impact.
• Strong programming proficiency in Python, TypeScript/JavaScript, Go, or a similar language, with disciplined use of testing, code quality, and maintainability practices.
• Hands-on experience with modern AI and LLM development, including context engineering, agentic system design, context-window management, token budgeting, and evaluation of AI quality, reliability, and safety.
• Experience designing model context through agentic retrieval and search, memory architectures, enterprise data grounding, and structured outputs.
• Experience engineering agent loops, multi-agent and sub-agent orchestration, and tool/function calling.
• Ability to optimize AI workloads for production cost and latency.
• Solid understanding of software architecture and systems design, including API design, event-driven patterns, and data modeling for scalability and extensibility.
• Experience developing or deploying applications with large-scale impact, such as a broad user base, high transaction volume, or organization-wide adoption.
• Experience integrating multiple systems and platforms, including REST or GraphQL APIs, CI/CD pipelines, cloud services, and enterprise tooling.