Senior AI Security Platform Engineer / Tech Lead

Key Responsibilities

• Design and implement production-grade agentic systems capable of autonomous vulnerability triage, analysis, and patch generation using LLM-based orchestration frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar tools.
• Build and maintain scalable backend platforms, APIs, SDKs, and developer tooling used by internal engineering teams and security practitioners.
• Define architectural best practices for agent workflow management, tool usage, memory, guardrails, observability, and safe automated actions.
• Integrate the platform with vulnerability scanners, SAST/DAST tools, SBOM systems, ticketing platforms, and CI/CD pipelines.
• Lead complex cross-functional technical initiatives across security engineering, platform infrastructure, AI/ML, and application development teams.
• Define technical roadmaps, contribute to engineering strategy, and help drive build-versus-buy decisions.
• Mentor engineers on agentic AI patterns, secure coding practices, DevSecOps automation, and platform reliability.
• Conduct design reviews, architecture decision records, and retrospectives to continuously improve engineering quality.
• Ensure the platform meets enterprise-grade standards for availability, performance, security, and auditability.
• Implement observability, including logging, metrics, tracing, and runbooks for agentic workflows in production.
• Partner with security teams to ensure the platform follows secure-by-design principles.
• Drive automation and feedback loops to improve MTTR for vulnerability remediation.

Required Qualifications

• 8+ years of professional software engineering experience building and operating production platforms, backend services, or developer tooling.
• Proven experience leading complex, cross-team technical initiatives from design through production delivery.
• Hands-on experience with AI/ML systems in production, especially LLM-based agents, orchestration, tool-calling, or prompt engineering at scale.
• Strong experience with Python and/or Go for backend development; Rust or Java experience is a plus.
• Strong knowledge of cloud platforms such as AWS, GCP, or Azure.
• Experience with infrastructure-as-code tools such as Terraform or Pulumi.
• Experience with Docker, Kubernetes, and modern CI/CD systems such as GitHub Actions, ArgoCD, or Tekton.
• Familiarity with agentic AI frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, LangChain, or similar technologies.
• Understanding of common vulnerability classes, including CVEs, CWEs, OWASP Top 10, and remediation strategies.
• Experience integrating APIs and tools across security, DevOps, and engineering platforms.
• Strong communication skills with the ability to influence technical direction across multiple teams.