Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today - ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone. Job Description The Team The Customer Excellence Group at ServiceNow works with customers to help them achieve their business outcomes by providing prescriptive guidance. As part of the Customer Excellence Group, you will work with our customers to drive consumption, adoption, and customer satisfaction and ultimately help our customers grow their business on the ServiceNow platform by getting them to see the value of their ServiceNow investment. Solution Architect - AI & Data, Expert Services As part of the Expert Services AI Practice, the Solution Architect - AI & Data will serve as a senior strategic and technical lead, reshaping how enterprise organizations adopt AI at the core of their operating models. This role goes beyond implementation - it bridges C-suite advisory, enterprise architecture, and organizational change to deliver lasting transformation outcomes. The Solution Architect - AI & Data will operate at the intersection of AI strategy, solution architecture, and customer success - leading engagements from transformation vision and use-case definition through architecture design, governance, adoption, and ongoing value realization. What you will do in this role: AI Strategy & Transformation Advisory - Lead enterprise AI transformation engagements - from opportunity identification and business case development through to operating model design and value realization. - Advise C-suite and senior stakeholders on AI strategy, prioritization frameworks, and transformation roadmaps tailored to their industry, maturity, and risk appetite. - Facilitate discovery workshops, current-state assessments, and future-state visioning sessions to establish a shared transformation agenda. - Define AI-enabled target operating models, including process redesign, workforce impact analysis, and governance structures. Solution Architecture & Delivery Leadership - Design end-to-end solution architectures spanning Now Assist, AI Agents, Agentic workflows, AI Control Tower, RAG, knowledge graphs, and enterprise integrations (A2A, MCP). - Lead scoping and solutioning for complex, multi-workload AI engagements - ensuring architectural integrity, scalability, and alignment to customer outcomes. - Provide hands-on architecture leadership during pilot and early-phase delivery, establishing patterns and standards for broader team execution. - Develop reusable practice IP: reference architectures, deployment patterns, transformation playbooks, and verticalized use-case catalogs. - Architect enterprise data catalog strategies using platforms defining target-state designs that align metadata management, data lineage, and governance structures to broader AI and business objectives. - Define integration patterns and architectural standards for connecting data catalog solutions across heterogeneous enterprise environments - cloud platforms, data warehouses, BI layers, and ServiceNow workflows. Data Architecture & Catalog Strategy - Lead the architectural design of enterprise data catalog programs - defining scope, platform selection criteria, governance operating models, and phased adoption roadmaps. - Advise on the strategic application of knowledge graph concepts, semantic technologies, and ontological frameworks (RDF, SPARQL) to enterprise data and AI use cases. - Shape data architecture principles and standards that underpin AI readiness - including data lineage, metadata quality, classification taxonomies, and access governance. - Translate complex data architecture requirements into clear, actionable designs that can be executed by delivery and technical teams. - Define success metrics and maturity benchmarks for data catalog programs, enabling customers to track progress and demonstrate value to executive stakeholders. AI Governance, Risk & Responsible AI - Define and embed AI governance frameworks covering data stewardship, model risk, bias controls, audit trails, and compliance postures. - Support customers in operationalize responsible AI practices aligned to regulatory requirements and internal policies. - Establish data governance frameworks that position metadata management, data stewardship, and knowledge graph capabilities as foundational trust layers for enterprise AI programs. - Guide customers in regulated industries on aligning data catalog and governance architectures to compliance and regulatory obligations, embedding controls into the design rather than as an afterthought. - Partner with AI Control Tower to establish monitoring, observability, and continuous optimization capabilities post-deployment. Adoption, Enablement & Change Management - Lead AI adoption strategies including readiness assessments, stakeholder engagement plans, AI literacy programmers, and change communications. - Define adoption KPIs and value realization metrics; track and report outcomes; provide consultative guidance for continuous optimization and expansion. - Coach customer teams to build internal AI capability, reducing dependency and accelerating long-term self-sufficiency. - Monitor adoption, usage, and value realization metrics post-deployment; provide recommendations for risk mitigation and growth. Practice Development & Thought Leadership - Collaborate cross-functionally with Sales, Solution Consulting, Customer Success, Platform, and Product teams to embed AI advisory across the customer lifecycle. - Build and maintain industry-specific AI advisory playbooks and frameworks - verticalized use-case catalogs, value models, governance templates, and deployment patterns - to support scalable, repeatable delivery. - Act as a thought-leader internally and externally: contribute to white papers, points-of-view, reference architectures, best-practice guides, and represent the organization at AI forums and customer briefings. - Support pre-sales by qualifying opportunities, shaping proposals, and presenting transformation vision to executive buyers. Qualifications To be successful in this role, you will have - Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. - 10+ years of experience in management consulting, enterprise architecture, or a senior technology advisory role, with a demonstrated focus on Artificial Intelligence, Machine Learning, or digital transformation at enterprise scale. - Experience facilitating executive AI strategy engagements - including visioning workshops, maturity assessments, and transformation roadmap sessions - where the output is a customer-owned AI narrative, not just a delivery plan. - Proven track record designing and managing complex, multi-stakeholder AI or digital-transformation engagements - including use-case definition, business case development, integration, data strategy, governance, and operational adoption. - Strong understanding of enterprise data architecture, data quality, knowledge management, integrations, and compliance and regulatory frameworks. - Demonstrated ability to architect enterprise data catalog and metadata management strategies, with knowledge of platforms. - Excellent communication and interpersonal skills - ability to articulate technical and business value to C-level executives, align stakeholders, and influence strategic decision-making. - AI domain knowledge required, including: Generative AI Skills, AI Agents and Agentic workflows, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Agent-to-Agent (A2A), and Model Context Protocol (MCP). - Experience working in fast-paced, dynamic environments with capability to manage ambiguity and tailor consulting deliverables to different customer maturity levels, from early adopters to AI-ready enterprises. - Working knowledge of knowledge graph principles, semantic technologies, and standards (RDF, SPARQL) as applied to enterprise data architecture. - Broad familiarity with the ServiceNow platform and modules (ITSM, CSM, FSM, HRSD, App Engine), ideally including implementation or architecture experience. ServiceNow certifications (Certified System Administrator, Certified Implementation Specialist, Certified Technical Architect) are desirable. - AI/ML certifications (e.g. AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, or equivalent) are desirable. - Background in one or more target industries - Financial Services, Healthcare, Public Sector, Manufacturing, or Retail - is highly desirable. Additional Information Work Personas We 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. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, 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. Accommodations We 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 globaltalentss@servicenow.com for assistance. Export Control Regulations For 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. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

Role Description - Design and develop enterprise-grade Generative AI and Agentic AI solutions. - Build AI-powered assistants, copilots, and intelligent workflow automation platforms. - Implement and optimize: - Large Language Models (LLMs) - Retrieval Augmented Generation (RAG) - Multi-Agent Systems - Semantic Search architectures - Develop AI orchestration frameworks for autonomous and collaborative AI agents. - Build scalable AI pipelines and microservices using Python and modern AI frameworks. - Design vector search and enterprise knowledge retrieval solutions. - Fine-tune prompts, optimize inference performance, and improve AI response quality. - Develop secure API integrations and event-driven AI architectures. - Collaborate with product, architecture, security, and engineering teams. - Ensure responsible AI governance, security, compliance, and observability standards. - Support deployment, monitoring, and continuous improvement of AI systems in production. Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

• Build internal tools and customer-facing features on top of LLMs (OpenAI, Claude, open-source models) • Design and maintain prompt chains, RAG pipelines, and agent workflows • Prototype rapidly — often going from idea to working demo in days, not sprints • Evaluate model outputs, set up evals, and iterate on quality • Wire AI into existing systems (APIs, databases, CRMs, Slack, etc.) • Own the full stack of a feature — frontend, backend, model integration, deployment

Role Description The Director of Engineering & AI-Enabled Delivery will lead a multidisciplinary, remote organization of ~150 professionals across software engineering, business analysis, design, content, and internal platforms. This leader will stabilize delivery, modernize engineering practices on cloud platforms, and build an AI-ready organization where teams use advanced tools responsibly and effectively to ship scalable products and services. Key Responsibilities - Organization and leadership: - Own the India-based engineering and delivery organization across SaaS products, custom development, and internal tools. - Design and implement a clear team structure (managers, tech leads, product/BA leads) with defined charters and accountability. - Build and develop high-performing managers and technical leaders; establish succession plans for key roles. - Engineering and AI-enabled delivery: - Define and enforce a standardized SDLC and delivery model for all teams, including cross-functional ceremonies and quality gates. - Establish cloud-native standards, reference architectures, and CI/CD pipelines for SaaS, web products, and internal tools. - Integrate AI tools into the development lifecycle (coding copilots, test generation, documentation assistants) with clear guardrails and human accountability. - Quality, reliability, and operations: - Set engineering standards for code quality, test automation, observability, and incident management. - Oversee architecture and design reviews for major initiatives; challenge and coach teams on scalability, security, and maintainability. - Own reliability metrics and continuous-improvement programs to reduce defects and incidents. - Talent development and culture: - Create and maintain a comprehensive skill matrix covering technical, AI, cloud, and cross-functional skills across all roles. - Define career paths and promotion criteria for engineers, managers, and cross-functional roles; make progression transparent and merit-based. - Build a culture of technical excellence, accountability, and continuous learning, including mentoring programs and internal knowledge sharing. - Stakeholder and strategic alignment: - Translate business goals into engineering roadmaps and capacity plans for SaaS products, client projects, and internal platforms. - Partner with US-based leadership, product, and business stakeholders to prioritize initiatives and communicate outcomes clearly. - Act as a visible champion for AI-enabled practices, helping non-technical leaders understand capabilities, risks, and ROI. Qualifications - 14-20 years in software engineering or platform engineering, including at least 5-7 years leading multi-team organizations. - Proven track record running engineering for SaaS or product organizations plus custom implementation or services work. - Strong hands-on background in modern architectures and cloud platforms (AWS, Azure, or GCP), including CI/CD and observability. - Demonstrated experience integrating AI tools into software delivery and coaching teams on responsible, effective usage. - Experience working with cross-functional teams (design, content, BAs) and aligning them around product outcomes. - Confident communicator with global stakeholders; comfortable challenging existing practices and leading change.