{"id":3861589,"url":"https://alion.io/job/servicenow-staff-software-aiml-engineer-2","title":"Staff Software AIML Engineer","company":{"id":97,"name":"ServiceNow","domain":"servicenow.com","url":"https://alion.io/company/servicenow","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":40,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Backend","role_family":"Backend","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Go","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"ServiceNow","optional":false},{"name":"TypeScript","optional":false},{"name":"Windsurf","optional":false},{"name":"Clean Architecture","optional":true},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-11-06T08:31:59Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-09T12:24:58Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone-freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow- helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.\nJoin us to put AI to work for people.\nStaff Software Engineer - AI Native Development\nAI Security Incubation & Innovation\nSecurity and Risk Engineering\nAbout the team\nThe 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.\nThis 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.\nThe Role\nAs a Staff Software Engineer - AI Native Development, you will be a hands-on technical leader responsible for the architecture, design, delivery, and evolution of major AI-powered software systems and subsystems.\nYou will combine deep full-stack software engineering expertise with strong AI/ML-native development skills to solve complex, ambiguous problems and build production-grade systems at scale. You will own significant technical areas end-to-end-from user experiences and APIs to distributed services, data and retrieval systems, AI/ML capabilities, and cloud infrastructure.\nBeyond your individual contributions, you will provide technical direction across a broader engineering area, make critical architecture and design decisions, establish engineering standards, and influence multiple engineers and teams. You will help shape how we build AI-native products and establish the technical foundation for the next generation of intelligent enterprise applications.\nThis is a role for an engineer who can operate effectively at both architectural altitude and implementation depth-someone who can define the direction, make the difficult technical decisions, and still dive into the code when needed.\nWhat You'll Own\nA major product or technical subsystem end-to-end, including its architecture, design, implementation, scalability, reliability, security, and ongoing evolution.\nThe technical vision and architecture for your area, including key design decisions and interfaces with other systems and teams.\nThe quality and business/technical outcomes of your subsystem, with measurable targets for reliability, performance, AI quality, latency, cost, and customer impact.\nThe architecture and engineering practices required to build AI-native applications at production scale.\nTechnical direction for engineers working within your area, providing guidance through architecture, design reviews, code reviews, and hands-on technical leadership.\nThe evolution of AI/ML capabilities such as agentic workflows, retrieval, model integration, evaluation, and intelligent automation within your product area.\nThe technical strategy for balancing AI capability, engineering complexity, reliability, security, latency, and cost.\nWhat You'll Do\nTechnical & Architectural Leadership\nTake highly ambiguous and complex problems and turn them into clear technical strategies, architectures, and executable plans.\nOwn the architecture of major systems or subsystems and drive them from concept through production at scale.\nMake sound technical decisions under uncertainty and clearly articulate architectural trade-offs.\nDefine system boundaries, interfaces, APIs, data flows, and integration patterns across multiple services and teams.\nDrive architecture and design reviews and establish a high engineering bar for scalability, reliability, security, maintainability, and performance.\nIdentify architectural risks and technical debt and drive long-term improvements across your area.\nInfluence technical direction beyond your immediate team through strong technical judgment and collaboration.\nFull-Stack Engineering\nRemain hands-on in building complex software across the stack, from frontend experiences and APIs to backend services, data systems, AI services, and cloud infrastructure.\nDesign scalable full-stack architectures using technologies such as React, TypeScript, Python, Java, Go, and modern cloud-native platforms.\nBuild distributed services, event-driven systems, APIs, databases, caching, messaging, and scalable data pipelines.\nEnsure systems are observable, resilient, secure, and operationally excellent in production.\nLead by example through high-quality implementation, testing, debugging, code reviews, and engineering practices.\nAI/ML-Native Development\nDefine and drive the adoption of AI-native architectures and engineering patterns across your technical area.\nDesign and build production-grade LLM and agentic systems, including:Multi-agent orchestration\nTool and function calling\nPlanning and reasoning loops\nContext and memory management\nRetrieval and grounding\nFailure recovery and resilience\nHuman-in-the-loop workflows\n\nIntegrate frontier models from providers such as OpenAI, Anthropic, Google, or equivalent platforms, making informed decisions around model capability, cost, latency, and reliability.\nDesign RAG and retrieval systems using embeddings, vector search, hybrid search, semantic retrieval, re-ranking, and enterprise data sources.\nEstablish robust AI evaluation strategies and measurable quality metrics for AI-powered functionality.\nDrive AI observability covering model quality, latency, cost, failures, hallucination/error rates, and system behavior.\nEstablish appropriate AI safety, security, governance, privacy, and guardrail mechanisms for production systems.\nEvaluate emerging AI capabilities and determine how and where they can create meaningful product or engineering value.\nTechnical Leadership & Influence\nProvide technical direction and mentorship to engineers across the workstream.\nLead complex engineering initiatives through influence rather than organizational authority.\nMentor senior and emerging engineers on architecture, system design, full-stack development, and production AI practices.\nPartner closely with product, design, platform, data, security, and other engineering organizations to translate customer problems into scalable technical solutions.\nFacilitate technical alignment across teams and resolve architectural disagreements through data, experimentation, and sound engineering judgment.\nEstablish reusable patterns, frameworks, libraries, and engineering practices that improve productivity across teams.\nHelp define the organization's approach to AI-native software development and AI-assisted engineering.\nAI-Assisted Development\nChampion effective use of AI coding agents and development tools such as Claude Code, Codex, Cursor, Windsurf, or equivalent technologies.\nEstablish engineering practices for using AI to accelerate development while maintaining code quality, security, testing, and accountability.\nIdentify opportunities to use AI across the software development lifecycle, including design, implementation, testing, debugging, documentation, and code review.\nShare learnings and establish best practices that enable teams to become more effective AI-native engineering organizations.\nWhat You Bring\nA strong track record of owning significant software systems or subsystems end-to-end in production.\nDeep full-stack engineering expertise with the ability to work across frontend, backend, APIs, data, AI services, and cloud infrastructure.\nStrong understanding of distributed systems, system architecture, data structures, algorithms, APIs, databases, scalability, reliability, and cloud-native development.\nExpert-level programming experience in Python, Java, Go, TypeScript, or equivalent languages.\nHands-on experience designing and delivering AI/ML-powered production systems.\nStrong practical knowledge of modern AI technologies including LLMs, RAG, embeddings, vector search, agentic workflows, tool calling, model evaluation, and AI observability.\nExperience taking ambiguous problems from concept and experimentation through reliable production systemsthat other engineers or product\n 8+ years of software engineering experience, or equivalent practical experience.\nExperience designing and delivering production software systems.\nExperience building or integrating AI/ML-powered applications in a production or near-production environment.\nModern AI experience: LLMs, RAG, embeddings, vector search, agentic workflows, model evaluation, or AI observability.\nStrong programming experience in Python and/or Java, Go, or a similar language.\nCloud-native technologies, distributed systems, APIs, databases, and scalable architectures.\nBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience.\nCybersecurity or security-product experience is a plus.\nFD21\n Work Personas\nWe approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.\nEqual Opportunity Employer\nServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.\nAccommodations\nWe strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact  for assistance.\nExport Control Regulations\nFor positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.\nFrom Fortune. ©2026 Fortune Media IP Limited. All rights reserved. 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