Principal Software Engineer Location Round Rock, TX Duration:
12
Months Responsibilities:
Design, develop, and deploy AI-powered capabilities across the Software Development Life Cycle (SDLC), including:
Specification-Driven Development:
Build workflows that translate well-defined specifications into secure, reliable, and verifiable software implementations.
AI-Generated Code Assurance:
Implement guardrails, policy enforcement, and verification mechanisms to ensure code generated by AI assistants and agents meets security and quality standards.
SDLC Skills and Agent Tooling:
Develop developer-assistance skills and verification tools that automate security checks, including design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release audit support. Integrate AI-powered solutions with enterprise systems, including source control platforms, CI/CD pipelines, ticketing systems, security scanning tools, identity management solutions, and internal platforms through APIs, webhooks, and protocols such as Model Context Protocol (MCP). Collaborate with engineering, security, product, and leadership teams to define requirements, evaluate technical trade-offs, and drive solution adoption across the organization. Apply sound software architecture and systems design principles, including clearly defined service boundaries, effective data modeling, secure-by-default configurations, observability, scalability, and extensibility. Develop and maintain production-ready AI solutions that improve software development efficiency, code quality, security assurance, and engineering productivity. Establish mechanisms to evaluate AI-generated outputs, enforce security policies, validate implementation quality, and ensure reliable performance throughout the SDLC. Optimize AI-powered workflows for performance, cost, latency, reliability, and scalability in enterprise environments. Ensure solutions adhere to enterprise security, compliance, and secure software development standards.
Required Skills and Qualifications:
Demonstrated experience developing, deploying, and maintaining AI-powered solutions in production environments, with measurable business or operational impact. Strong programming proficiency in Python, TypeScript, JavaScript, Go, or similar languages, with a focus on code quality, testing, maintainability, and software engineering best practices. Hands-on experience with modern AI and Large Language Model (LLM) development, including:
Context Engineering:
Designing and managing model context windows, agentic retrieval and search, memory architectures, enterprise data grounding, and structured outputs.
Agentic System Design:
Engineering agent loops, orchestrating multi-agent and sub-agent workflows, and implementing tool/function calling.
Context Window and Token Management:
Managing context windows, budgeting tokens, and optimizing inference costs and latency for production workloads.
AI Evaluation and Reliability:
Evaluating AI system quality, accuracy, reliability, security, and safety. Strong understanding of software architecture and systems design, including API design, event-driven architectures, data modeling, scalability, and extensibility. Experience developing and deploying applications that support large-scale usage, high transaction volumes, or organization-wide adoption. Proven experience integrating multiple systems and platforms using REST/GraphQL APIs, CI/CD pipelines, cloud services, and enterprise development tools. Ability to independently manage the full solution delivery lifecycle, including requirements analysis, architecture and design, implementation, testing, deployment, and stakeholder engagement. Excellent communication and collaboration skills, with the ability to explain complex technical concepts to engineering teams, business stakeholders, and leadership. Working knowledge of secure software development practices and experience designing solutions that meet enterprise security and compliance requirements.
Preferred Skills and Qualifications:
Experience applying AI to security-focused domains, including application security, DevSecOps, automated code analysis, threat modeling, firmware security, or software supply-chain security. Familiarity with secure-by-design and secure-by-default principles, including relevant security frameworks such as the OWASP Top 10 for LLM Applications and NIST Secure Software Development Framework (SSDF). Hands-on experience with Model Context Protocol (MCP), developing AI agent skills and tools, or extending AI coding assistants such as Claude Code, GitHub Copilot, Cursor, or Devin. Experience with AI evaluation frameworks, guardrails, prompt and response caching strategies, and Large Language Model Operations (LLMOps) in production environments. Knowledge of automated security testing, static application security testing (SAST), dynamic application security testing (DAST), software composition analy
