Servicenow Architect(Solution Architect)

Job description:-

This role combines deep ServiceNow platform expertise with specialized knowledge in Artificial Intelligence and Machine Learning (AI/ML), particularly leveraging the platform's native intelligence features. The ideal candidate will design and implement AI/ML solutions within the ServiceNow ecosystem, identify practical use cases, and translate technical capabilities into business value for key stakeholders and Process SMEs.

Responsibilities

AI/ML Strategy and Architecture

Architect AI/ML Solutions: Design and lead the technical architecture for intelligent solutions within ServiceNow, utilizing native features like Predictive Intelligence and Now Assist, as well as integrating with external AI platforms.

Define Use Cases: Collaborate with business leaders and Process SMEs to define compelling AI/ML use cases that address critical business challenges and drive digital transformation.

Roadmap Development: Create a strategic roadmap for AI/ML adoption on the ServiceNow platform, outlining future capabilities and infrastructure investments.

Data and MLOps: Work with data engineers and ML engineers to establish data pipelines, implement MLOps practices, and manage the full lifecycle of ML models, from experimentation to production.

Ethical AI Governance: Ensure ethical AI practices, data privacy, and security are embedded in all AI/ML solutions.

ServiceNow Platform Expertise

Platform Design: Serve as the technical authority on the ServiceNow platform, designing scalable, secure, and high-performing solutions that adhere to best practices, such as the Common Service Data Model (CSDM).

Integration Leadership: Architect and oversee integrations between ServiceNow, enterprise data platforms, and third-party systems using tools like Integration Hub, APIs, and MID Servers.

Core Module Enhancement: Guide the enhancement and optimization of core ServiceNow modules (ITSM, ITOM, HRSD, etc.) with AI/ML functionality.

Performance Optimization: Implement strategies to ensure the overall health, performance, and scalability of the ServiceNow instance.

Business & Stakeholder Engagement

Technical Advisory: Act as a trusted technical advisor to C-suite executives, business leaders, and product owners.

Stakeholder Communication: Translate complex technical concepts related to AI/ML into understandable business outcomes for both technical and non-technical audiences.

Change Management: Work with delivery teams to ensure successful user adoption of new AI-powered features, addressing the impact of automation on workflows.

Mentorship: Provide guidance and mentorship to development teams on ServiceNow best practices, AI capabilities, and emerging technologies.

Qualifications:-

8+ years of experience in enterprise software architecture, with a minimum of 5 years focused on the ServiceNow platform.

3+ years of hands-on experience designing and deploying AI/ML solutions, preferably within the ServiceNow ecosystem using products like Predictive Intelligence, Now Assist, and Virtual Agent.

Proven experience in architecting and delivering production-ready ML systems in areas such as search, NLP, recommendation, and classification.

Proficiency with ML frameworks (e.g., TensorFlow, PyTorch), cloud AI services (e.g., AWS SageMaker, Azure ML), and MLOps tools.

Extensive experience with ServiceNow development, scripting (JavaScript), integrations (REST/SOAP), and data models (CSDM).

Strong communication and leadership skills, with the ability to influence technical and business stakeholders.

ServiceNow Certified Technical Architect (CTA) or equivalent experience is highly preferred.

Recommendations for Process SMEs

For Process SMEs, the arrival of a ServiceNow Architect with AI/ML experience represents a significant opportunity to optimize and transform business processes. The key is to focus on understanding AI's potential and preparing for its implementation.

1. Shift your mindset from "what to automate" to "what to augment" Identify Predictive Opportunities: Think about where historical data can predict future events. Can AI predict ticket volumes for staffing, incident categories for auto-routing, or potential service outages before they occur?

Augment Decision-Making: Consider how AI can provide recommendations to improve process efficiency and accuracy. For example, for a support agent, an ML-driven system could suggest the next best action or relevant knowledge article based on the incident description.

1. Focus on data quality and readiness

Prioritize Data Cleanup: Work with the architect to identify critical data inconsistencies or gaps. Explain how data is used and its impact on the process. AI/ML models are only as good as the data they are trained on, so data quality is paramount.

Document Data Lineage: Document where your process data comes from, how it's used, and who is responsible for it. This will help the architect design proper data pipelines and governance frameworks.

1. Collaborate on defining clear business outcomes

Define Success Metrics: Instead of focusing on a technical feature, articulate the desired business outcome. For example, rather than "implement predictive intelligence," a better goal would be "reduce the average time to resolve high-priority incidents by 15% using predictive routing".

Quantify the Impact: Work with the architect to provide quantitative data that will be used to build the business case. This includes ticket volumes, resolution times, costs, and user satisfaction metrics.

1. Prepare for an iterative approach

Embrace Change: AI/ML solutions are rarely "set and forget." The models will need to be continuously trained and refined based on new data and feedback.

Provide Continuous Feedback: Your expertise as a Process SME is invaluable. Be prepared to provide feedback on the accuracy and performance of AI-powered features, acting as a crucial part of the feedback loop for model improvement.

1. Become an AI advocate for your process

Promote User Adoption: Be an internal champion for the new AI features within your teams. Help build confidence in the technology and educate end-users on how to interact with AI-powered features like virtual agents or recommendation systems.

Support Ethical Implementation: Help the architect ensure that the AI solutions for your process are fair, transparent, and do not introduce bias. You provide the critical human context that technical teams need to ensure responsible AI development.