Principal or Lead AI Engineer

About the role

*** DO NOT APPLY if you are NOT willing to work on W2 (NO C2C or NO 1099) *** NO sponsorships

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.