Principal Software Engineer

• 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.

• 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 analysis (SCA), dependency management, and software supply-chain security.
• Bachelor’s or master’s degree in Computer Science, Software Engineering, Cybersecurity, or a related field, or equivalent practical experience.
• Security Harness Engineering