Senior AI Engineer

SecurityScorecard is the global leader in cybersecurity ratings, providing organizations with tools to manage their cybersecurity risks. They are seeking a Senior AI Engineer to design and implement AI-powered product features, integrating cutting-edge AI capabilities to enhance security insights for customers.

Responsibilities:

• Design, build, and ship customer-facing AI-powered product features using TypeScript, Go, and AWS, owning quality and reliability end-to-end
• Architect and implement agentic workflows, LLM pipelines, and intelligent automation that help customers surface faster, more actionable security insights
• Integrate foundation models into existing product surfaces with a practical eye toward latency, cost, output quality, and evaluation
• Use AI-native development tools (Cursor, Claude Code, and similar) as a core part of your workflow, setting the standard for how this team builds
• Lead architecture and code reviews with a focus on production-grade reliability, security, and maintainability
• Partner with product and design to translate user needs into compelling AI-driven experiences, contributing product judgment alongside technical execution
• Own projects end-to-end from concept through production, including monitoring, iteration, and improvement post-launch
• Stay current with the AI tooling and foundation model landscape and bring relevant advances into our roadmap and practices

Requirements:

• 5+ years of professional software engineering experience with a strong track record shipping customer-facing product features
• 2+ years of hands-on experience building AI-powered applications in production, including LLM integration, prompt design, and output evaluation
• Proficiency in TypeScript and strong backend engineering fundamentals
• Demonstrated ability to own complex projects end-to-end across the full software development lifecycle
• Experience designing and operating distributed systems in cloud environments (AWS preferred)
• Hands-on experience with CI/CD, containerization (Docker, Kubernetes), and production deployment practices
• Strong computer science fundamentals including systems design, data structures, and networking
• Fluency with AI-native development workflows including AI-assisted coding tools as part of day-to-day engineering
• Clear, direct communication with both technical and non-technical stakeholders
• Experience with agentic AI systems, multi-agent orchestration, or retrieval-augmented generation (RAG)
• Familiarity with LLM evaluation frameworks and production observability for AI systems
• Background in cybersecurity, threat intelligence, or compliance-focused products
• Experience with high-scale data systems and large structured datasets
• Full-stack delivery experience and comfort working across the product surface