Principal AI Engineer (Agentic AI)-Enterprise Scalability/ Security Harness (W2 ONLY, LOCALS to TX)

*** STRICT W2 (NO C2C/ NO S-corp/ NO 1099) ***

100% ONSITE project in Austin TX

Enterprise scalability, Security Harness Engineering, and hands-on AI software engineering.

Responsibilities

• Design, build, and deploy AI-powered capabilities across the software development lifecycle (SDLC), including:
• Spec-Driven Development (SDD) workflows that translate well-formed specifications into secure, verifiable implementations.
• Assurance of AI-generated code, including guardrails, policy enforcement, validation, and verification for code produced by AI assistants and autonomous agents.
• SDLC skills and agent tooling, including developer-assistance capabilities and automated verification skills for security checks such as design reviews, dependency and software supply-chain analysis, static and dynamic analysis orchestration, and release-audit support.
• Integrate AI solutions with enterprise systems, including source control, CI/CD, ticketing, security-scanning platforms, identity systems, and internal applications through APIs, webhooks, and protocols such as MCP (Model Context Protocol).
• Partner with engineering, security, product, and leadership teams to define requirements, evaluate technical trade-offs, establish priorities, and drive solution adoption.
• Apply strong architecture and systems-design principles, including clearly defined service boundaries, appropriate data models, secure-by-default configurations, observability, scalability, and extensibility.

Required Skills and Qualifications

• Demonstrated experience developing and deploying AI-based solutions in production environments, with measurable business, technical, or operational impact.
• Strong programming proficiency in Python, TypeScript/JavaScript, Go, or a similar modern programming language, with demonstrated adherence to software engineering best practices, including testing, code quality, maintainability, and documentation.
• Hands-on experience with modern AI/LLM development, including:
• Context engineering — designing and optimizing the information provided to model context windows, including agentic retrieval and search, memory architectures, enterprise-data grounding, and structured outputs.
• Agentic system design — engineering agent loops, multi-agent and sub-agent orchestration, and tool/function calling.
• Context-window management and token budgeting, including optimization of cost, latency, throughput, and reliability for production workloads.
• AI system evaluation, including quality, reliability, safety, and performance assessment.
• Strong understanding of software architecture and systems design, including API design, event-driven architectures, distributed systems, and scalable data modeling.
• Experience developing and/or deploying applications at large scale, supporting broad user bases, high transaction volumes, or organization-wide adoption.
• Experience integrating multiple systems and platforms, including REST/GraphQL APIs, CI/CD pipelines, cloud services, security platforms, and enterprise tooling.
• Demonstrated ability to work independently across the full software delivery lifecycle, including requirements analysis, solution architecture, implementation, testing, deployment, monitoring, and stakeholder engagement, with accountability for outcomes.
• Strong communication and collaboration skills, with the ability to explain complex technical concepts to both engineering and business audiences.
• Working knowledge of secure software development practices and experience designing solutions that satisfy enterprise security, privacy, risk, and compliance requirements.

Preferred Skills and Qualifications

• Experience applying AI to security domains, including application security, DevSecOps, code analysis, threat modeling, firmware security, or software supply-chain security.
• Familiarity with secure-by-design and secure-by-default principles and relevant frameworks, including OWASP and the OWASP Top 10 for LLM Applications, as well as NIST SSDF.
• Experience with MCP (Model Context Protocol), developing 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/response caching, observability, and LLMOps in production environments.
• Bachelor's or master's degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.