Senior AI Software Engineer – Agentic AI / Security Harness Engineering

Experience: 8+ years of software engineering experience required
Critical Requirement: Minimum 12 months of hands-on production AI-agent engineering experience

Required Skills and Qualifications

• Minimum 12 months of hands-on experience building AI agents that are running in production for an employer or client.
• Production agent experience must involve substantive agent engineering such as:
  • Agent loops.
  • Tool/function calling.
  • Multi-step orchestration.
  • Autonomous or semi-autonomous decision workflows.
  • Production deployment and operational ownership.
• Candidate experience should demonstrate specific production evidence, preferably including:
  • Named or clearly described production system/use case.
  • Dates of hands-on agent development.
  • Users, transactions, workflows, requests, documents, or other relevant scale/volume.
  • Measurable technical, operational, or business results.
• RAG chatbots/search solutions alone do not satisfy the production-agent requirement.
• Classic machine learning, standalone RAG/search, proofs of concept, personal projects, research projects, or experimental prototypes do not count toward the required 12 months of production agent experience.
• Using AI coding assistants such as GitHub Copilot, Claude Code, Cursor, or similar tools does not by itself constitute AI-agent engineering experience.
• 8+ years of professional software-engineering experience with direct ownership of production systems.
• Demonstrated experience developing and deploying AI-based solutions in production environments with measurable business or operational impact.
• Strong programming proficiency in Python, TypeScript/JavaScript, Go, or comparable languages, with strong software-engineering practices covering testing, code quality, modularity, maintainability, and production support.
• Hands-on experience with modern AI/LLM development, including:
  • Context engineering: designing what enters and informs the model context, including enterprise grounding, agentic retrieval/search, memory architectures, and structured outputs.
  • Agentic system design: agent-loop engineering, tool/function calling, multi-step orchestration, and multi-agent/sub-agent patterns.
  • Context-window management and token budgeting.
  • Cost and latency optimization for production AI workloads.
  • Evaluation of AI-system quality, reliability, safety, and policy compliance.
• Solid understanding of software architecture and distributed-system design, including API design, service boundaries, event-driven patterns, data modeling, scalability, resiliency, and observability.
• Experience developing and/or deploying applications with significant enterprise-scale impact, such as:
  • Broad internal or external user adoption.
  • High transaction/request volume.
  • Large enterprise deployment.
  • Organization-wide adoption.
  • Business-critical production workloads.
• Experience integrating AI solutions across multiple enterprise systems and platforms using REST/GraphQL APIs, event-driven integrations, CI/CD systems, cloud services, identity platforms, security tooling, and internal enterprise services.
• Demonstrated ability to independently own the full delivery lifecycle from requirements and architecture through implementation, deployment, production monitoring, and stakeholder engagement.
• Strong communication and collaboration skills with the ability to explain architecture, implementation choices, risks, and trade-offs to engineering, security, product, and business stakeholders.
• Working knowledge of secure-development practices and experience designing solutions that meet enterprise security and compliance requirements.
  

Responsibilities

• Design, build, and deploy production AI-powered capabilities across the software development lifecycle (SDLC), including:
  • Spec-Driven Development workflows that translate well-formed specifications into secure, verifiable implementations.
  • Assurance of AI-generated code through guardrails, policy enforcement, automated testing, security validation, and verification of code produced by AI assistants and agents.
  • SDLC skills and agent tooling that provide developer assistance as well as automated security verification, including design review, dependency and software supply-chain analysis, static/dynamic analysis orchestration, security gates, and release-audit support.
• Design, build, and operate production AI-agent systems, including:
  • Agent loops involving reasoning, planning, tool execution, evaluation, and iterative decision-making.
  • Tool/function calling against enterprise applications and services.
  • Multi-step workflow orchestration.
  • Multi-agent and/or sub-agent orchestration where appropriate.
  • Human-in-the-loop and approval workflows for sensitive or high-risk operations.
  • Failure handling, observability, auditability, and operational controls required for reliable production deployment.
• Build Security Harness capabilities around AI-assisted development and autonomoagentic workflows, including:
  • Policy enforcement and guardrails.
  • Controlled tool access and authorization.
  • AI-generated code assurance.
  • Automated security verification.
  • SAST, DAST, dependency, and software supply-chain analysis.
  • Security and release gates.
  • Threat modeling and secure design reviews.
  • Audit evidence, observability, and release validation.
• Integrate AI agents and services with enterprise systems including source control, CI/CD platforms, ticketing systems, security scanning tools, identity platforms, data services, and internal applications through APIs, webhooks, event-driven integrations, and protocols such as MCP (Model Context Protocol).
• Design context-engineering strategies for production AI systems, including enterprise grounding, agentic retrieval/search, memory architectures, structured outputs, context-window management, and token budgeting.
• Optimize AI systems for production considerations including latency, cost, reliability, scalability, availability, security, and maintainability.
• Define and implement evaluation approaches for AI-agent quality, reliability, safety, tool-use correctness, and policy compliance.
• Partner with engineering, security, product, architecture, and leadership stakeholders to define requirements, evaluate technical trade-offs, and support enterprise adoption.
• Apply sound architecture and systems-design practices including well-defined service boundaries, scalable APIs, appropriate data models, event-driven patterns, secure defaults, observability, resiliency, and extensibility.
• Own solutions across the complete delivery lifecycle, including requirements analysis, architecture, implementation, testing, deployment, production support, monitoring, and continuous improvement.