Principal AI Engineer - Enterprise Scalability/ Fortune 100 exposure, Security Harness, Agentic AI

*** 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.