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Staff Software AIML Engineer

ServiceNow · Posted Nov 6

Cloud-based workflow automation and artificial intelligence platform services

Hyderabad, IndiaFull-timeOnsiteLead/Staff8+ years₹50.0L–₹85.0L yearly34 applicants
Software DevelopmentCloud ComputingArtificial IntelligenceWorkflow AutomationSaaSPublic Company
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About the role

The Security and Risk Engineering organization at ServiceNow builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. This role sits within an AI Security Incubation & Innovation team doing zero-to-one work on a new class of exposure analysis that ranks security work by exploitability rather than raw severity. As a Staff Software Engineer – AI Native Development, the engineer acts as a hands-on technical leader owning the architecture, design, delivery, and evolution of major AI-powered full-stack systems, including LLM and agentic workflows, RAG and retrieval systems, and the surrounding cloud infrastructure.

What you will do

  • A major product or technical subsystem end-to-end, including its architecture, design, implementation, scalability, reliability, security, and ongoing evolution.
  • The technical vision and architecture for your area, including key design decisions and interfaces with other systems and teams.
  • The quality and business/technical outcomes of your subsystem, with measurable targets for reliability, performance, AI quality, latency, cost, and customer impact.
  • The architecture and engineering practices required to build AI-native applications at production scale.
  • Technical direction for engineers working within your area, providing guidance through architecture, design reviews, code reviews, and hands-on technical leadership.
  • The evolution of AI/ML capabilities such as agentic workflows, retrieval, model integration, evaluation, and intelligent automation within your product area.
  • The technical strategy for balancing AI capability, engineering complexity, reliability, security, latency, and cost.
  • Take highly ambiguous and complex problems and turn them into clear technical strategies, architectures, and executable plans.
  • Own the architecture of major systems or subsystems and drive them from concept through production at scale.
  • Make sound technical decisions under uncertainty and clearly articulate architectural trade-offs.
  • Define system boundaries, interfaces, APIs, data flows, and integration patterns across multiple services and teams.
  • Drive architecture and design reviews and establish a high engineering bar for scalability, reliability, security, maintainability, and performance.
  • Identify architectural risks and technical debt and drive long-term improvements across your area.
  • Influence technical direction beyond your immediate team through strong technical judgment and collaboration.
  • Remain hands-on in building complex software across the stack, from frontend experiences and APIs to backend services, data systems, AI services, and cloud infrastructure.
  • Design scalable full-stack architectures using technologies such as React, TypeScript, Python, Java, Go, and modern cloud-native platforms.
  • Build distributed services, event-driven systems, APIs, databases, caching, messaging, and scalable data pipelines.
  • Ensure systems are observable, resilient, secure, and operationally excellent in production.
  • Lead by example through high-quality implementation, testing, debugging, code reviews, and engineering practices.
  • Define and drive the adoption of AI-native architectures and engineering patterns across your technical area.
  • Design and build production-grade LLM and agentic systems, including:
  • Multi-agent orchestration
  • Tool and function calling
  • Planning and reasoning loops
  • Context and memory management
  • Retrieval and grounding
  • Failure recovery and resilience
  • Human-in-the-loop workflows
  • Integrate frontier models from providers such as OpenAI, Anthropic, Google, or equivalent platforms, making informed decisions around model capability, cost, latency, and reliability.
  • Design RAG and retrieval systems using embeddings, vector search, hybrid search, semantic retrieval, re-ranking, and enterprise data sources.
  • Establish robust AI evaluation strategies and measurable quality metrics for AI-powered functionality.
  • Drive AI observability covering model quality, latency, cost, failures, hallucination/error rates, and system behavior.
  • Establish appropriate AI safety, security, governance, privacy, and guardrail mechanisms for production systems.
  • Evaluate emerging AI capabilities and determine how and where they can create meaningful product or engineering value.
  • Provide technical direction and mentorship to engineers across the workstream.
  • Lead complex engineering initiatives through influence rather than organizational authority.
  • Mentor senior and emerging engineers on architecture, system design, full-stack development, and production AI practices.
  • Partner closely with product, design, platform, data, security, and other engineering organizations to translate customer problems into scalable technical solutions.
  • Facilitate technical alignment across teams and resolve architectural disagreements through data, experimentation, and sound engineering judgment.
  • Establish reusable patterns, frameworks, libraries, and engineering practices that improve productivity across teams.
  • Help define the organization's approach to AI-native software development and AI-assisted engineering.
  • Champion effective use of AI coding agents and development tools such as Claude Code, Codex, Cursor, Windsurf, or equivalent technologies.
  • Establish engineering practices for using AI to accelerate development while maintaining code quality, security, testing, and accountability.
  • Identify opportunities to use AI across the software development lifecycle, including design, implementation, testing, debugging, documentation, and code review.
  • Share learnings and establish best practices that enable teams to become more effective AI-native engineering organizations.
  • A strong track record of owning significant software systems or subsystems end-to-end in production.
  • Deep full-stack engineering expertise with the ability to work across frontend, backend, APIs, data, AI services, and cloud infrastructure.
  • Strong understanding of distributed systems, system architecture, data structures, algorithms, APIs, databases, scalability, reliability, and cloud-native development.
  • Expert-level programming experience in Python, Java, Go, TypeScript, or equivalent languages.
  • Hands-on experience designing and delivering AI/ML-powered production systems.
  • Strong practical knowledge of modern AI technologies including LLMs, RAG, embeddings, vector search, agentic workflows, tool calling, model evaluation, and AI observability.
  • Experience taking ambiguous problems from concept and experimentation through reliable production systemsthat other engineers or product

Skills used in this role

What the employer is looking for

  • 8+ 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 experience is a plus.

Benefits and support

  • Compensation and benefits are detailed in the job posting

About ServiceNow

ServiceNow logo

ServiceNow

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ServiceNow provides an end-to-end cloud-based platform and workflow automation solutions that help enterprises digitize and unify operations. Its platform leverages artificial intelligence and machine learning to streamline IT service management, customer service, and business processes.

Industry
Software Development
Company size
10001+ employees
Founded
2004
Location
Santa Clara, California, USA
Funding stage
Public Company

Funding

Public Company · $4.08B raised

Sequoia CapitalJMI EquityGreylockFoundation Capital
  • 2026-04-17Post IPO Debt$4.00B

Leadership

BM
Bill McDermott

Chairman and Chief Executive Officer

GM
Gina Mastantuono

President and Chief Financial Officer

AZ
Amit Zavery

President, Chief Product Officer and Chief Operating Officer

PC
Pat Casey

Chief Technology Officer and EVP, DevOps

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