AI Software Engineer

• Designs, codes, tests, debugs and documents software according to client’s systems quality standards, policies and procedures.
• Analyzes business needs and creates software solutions.
• Responsible for preparing design documentation. Prepares test data for unit, string and parallel testing.
• Evaluates and recommends software and hardware solutions to meet user needs. Resolves customer issues with software solutions and responds to suggestions for improvements and enhancements.
• Works with business and development teams to clarify requirements to ensure testability. Drafts, revises, and maintains test plans, test cases, and automated test scripts.
• Executes test procedures according to software requirements specifications Logs defects and makes recommendations to address defects.
• Retests software corrections to ensure problems are resolved. Documents evolution of testing procedures for future replication.
• May conduct performance and scalability testing.

Responsibilities:

• Design, build, and deploy AI-powered capabilities across the SDLC, including: Spec Driven Development workflows that support the translation of well-formed specifications into secure, verifiable implementations Assurance of AI-generated code - guardrails, policy enforcement, and verification for code produced by AI assistants and agents SDLC skills and agent tooling - developer-assist skills as well as verification skills that perform automated security checks (design review, dependency and supply-chain analysis, static/dynamic analysis orchestration, release audit support)

• Integrate solutions with enterprise systems - source control, CI/CD, ticketing, security scanning, identity, and internal platforms - through APIs, webhooks, and protocols such as MCP (Model Context Protocol)
• Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate trade-offs, and support solution adoption
• Apply sound architecture and systems design practices: well-defined service boundaries, appropriate data models, secure defaults, observability, and extensibility

• Demonstrated experience developing and deploying AI-based solutions in production environments, with measurable business or operational impact
• Strong programming proficiency (e.g., Python, TypeScript/JavaScript, Go, or similar) and adherence to software engineering best practices, including testing, code quality, and maintainability

• Hands-on experience with modern AI/LLM development, including: Context engineering - designing what informs the model’s context window, including agentic retrieval and search, memory architectures, grounding in enterprise data, and structured outputs Agentic system design - agent loop engineering, multi-agent and sub-agent orchestration, and tool/function calling Context window management and token budgeting, including cost and latency optimization for production workloads Evaluation of AI system quality, reliability, and safety

• Solid understanding of software architecture and systems design, including API design, event-driven patterns, and data modeling for scalability and extensibility
• Experience developing and/or deploying applications with large-scale impact (broad user base, high transaction volume, or organization-wide adoption)
• Experience integrating with multiple systems and platforms (REST/GraphQL APIs, CI/CD pipelines, cloud services, enterprise tooling)
• Demonstrated ability to work independently across the full delivery lifecycle - requirements analysis, solution design, implementation, deployment, and stakeholder engagement - with accountability for results
• Strong communication and collaboration skills, with the ability to convey technical concepts to both engineering and business audiences
• Working knowledge of secure development practices and experience designing solutions that meet enterprise security and compliance requirements

Preferred Skills and Qualifications:

• Experience applying AI within a security domain - application security, DevSecOps, code analysis, threat modeling, firmware security or software supply-chain security
• Familiarity with secure-by-design / secure-by-default principles and relevant frameworks (e.g., OWASP, including the OWASP Top 10 for LLM Applications; NIST SSDF)
• Experience with MCP (Model Context Protocol), building agent skills and tools, or extending AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor, Devin)
• Experience with AI evaluation frameworks, guardrails, prompt/response caching strategies, and LLMOps in production, * Bachelor’s or master’s degree in computer science or a related field, or equivalent practical Security Harness Engineering