Senior Software AIML Engineer

AI Security Incubation & Innovation

Security and Risk Engineering

About the team

The Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning.

This is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability—where an attacker could realistically get in—rather than raw severity. The architecture is evolving, and this role helps define what good looks like.

The Role

As a Senior Software Engineer – AI Native Development, you will design, build, and operate next-generation AI-powered applications and platforms. You will combine strong full-stack software engineering fundamentals with hands-on expertise in modern AI/ML technologies to build production-grade experiences powered by LLMs, agents, retrieval, and intelligent automation.

You will work across the technology stack—from user experiences and APIs to distributed services, data and retrieval systems, AI/ML workflows, and cloud infrastructure. You will be expected to use AI-native development practices to accelerate engineering productivity while maintaining high standards for scalability, reliability, security, and quality.

This role is ideal for an engineer who enjoys solving complex problems end-to-end and is excited about applying AI as a core engineering capability, not simply as an add-on to traditional software.

What You'll

Own

• Design and build end-to-end full-stack applications, including frontend experiences, backend services, APIs, data layers, and cloud-native infrastructure.
• Build production-grade AI/ML-powered capabilities using LLMs, RAG, embeddings, semantic search, agentic workflows, and intelligent decision-making.
• Design and implement agentic architectures, including tool calling, orchestration, planning loops, memory, context management, and failure recovery.
• Develop reliable AI-powered APIs and services that integrate frontier models and enterprise data securely and efficiently.
• Build retrieval and grounding pipelines using vector search, hybrid search, semantic retrieval, re-ranking, and contextual enrichment.
• Establish evaluation and observability mechanisms to measure AI quality, accuracy, latency, cost, reliability, and safety.
• Take features from concept and prototype through production deployment and ongoing operation, with ownership of quality and reliability.
• Work with product, design, platform, data, security, and other engineering teams to translate ambiguous problems into scalable technical solutions.
• Contribute to architecture and design decisions, code reviews, engineering standards, and technical direction.
• Mentor engineers and help raise the bar on full-stack engineering and production AI development practices.

What You'll Do

• Design and develop scalable, maintainable frontend applications and backend services using REST/GraphQL APIs, microservices, event-driven services, and distributed systems.
• Work with modern frontend technologies such as React, TypeScript, JavaScript, or equivalent frameworks.
• Build cloud-native applications with strong focus on scalability, performance, reliability, and security.
• Own software delivery across development, testing, deployment, monitoring, and production operations.
• Build applications leveraging LLMs, generative AI, embeddings, RAG, semantic search, and agentic workflows.
• Integrate frontier AI models and SDKs such as OpenAI, Anthropic, Google, or equivalent platforms.
• Apply prompt engineering, structured outputs, function/tool calling, context engineering, and model selection to real-world applications.
• Design agent workflows that can reason, use tools, retrieve information, execute actions, and recover from failures.
• Build AI evaluation frameworks and automated tests to measure model and application quality.
• Balance model capability, accuracy, latency, scalability, and cost when selecting and integrating AI models.
• Apply AI safety, security, privacy, governance, and guardrail practices to production AI systems.
• Explore and adopt emerging AI technologies and rapidly turn promising capabilities into production-ready solutions.

AI-Native Engineering Practices

• Use AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, Windsurf, or equivalent tools as part of the software development lifecycle.
• Apply AI to improve engineering productivity across coding, testing, debugging, documentation, code review, and system design.
• Develop effective workflows for collaborating with coding agents while maintaining engineering quality and accountability.
• Help establish best practices for AI-native software development across the engineering organization.
• 5+ years of software engineering experience building and operating production-quality software.
• Strong understanding of software engineering fundamentals, data structures, algorithms, design patterns, APIs, and distributed systems.
• Hands-on experience developing full-stack applications, with strength in both frontend and backend engineering.
• Strong programming experience in one or more of Python, Java, Go, TypeScript, JavaScript, or similar languages.
• Experience with modern frontend development, preferably React and TypeScript or equivalent technologies.
• Experience designing and building cloud-native applications, scalable APIs, microservices, databases, and distributed systems.
• Hands-on experience building or integrating AI/ML-powered applications in production or near-production environments.
• Practical understanding of modern AI concepts including LLMs, embeddings, RAG, vector databases/search, semantic search, agents, tool calling, and model evaluation.
• Experience integrating one or more frontier AI model platforms/SDKs such as OpenAI, Anthropic, or Google.
• Ability to take an AI/ML prototype and turn it into a reliable, scalable, maintainable production solution.
• Strong debugging, problem-solving, and system-design skills.
• Strong communication and collaboration skills with the ability to work effectively across engineering, product, design, data, and platform teams.
• Demonstrated ownership of technical decisions and a track record of improving code quality and engineering practices.

Nice to Have

• Experience designing multi-agent systems and agent orchestration frameworks.
• Experience with AI evaluation, observability, guardrails, and responsible AI practices.
• Experience with vector databases, hybrid retrieval, re-ranking, knowledge graphs, or enterprise search.
• Experience with ML pipelines, MLOps, model monitoring, or inference optimization.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with Kubernetes, containers, CI/CD, and infrastructure-as-code.
• Experience with AI coding agents such as Claude Code, Codex, Cursor, or Windsurf.
• Contributions to open-source AI/ML or developer tooling projects.
• Experience working on cybersecurity, identity, risk, enterprise SaaS, or other complex domain platforms is a plus.

Qualifications

• 5+ years of software engineering experience, or equivalent practical experience.
• Experience designing and delivering production software systems.
• Experience building or integrating AI/ML-powered applications in a production or near-production environment.
• Modern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.
• Strong programming experience in Python and/or Java, Go, or a similar language.
• Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.
• Cybersecurity or security-product experien