ServiceNow Senior Technical Consultant - AI

Roles and Responsibilities

Solution and Stakeholder Leadership

Translate business outcomes and documented requirements into AI solutions that are secure, governed, explainable, and aligned to platform best practices

Identify and qualify AI use cases with clients, assessing data readiness, deflection or cycle‑time potential, risk tolerance, and human‑in‑the‑loop requirements. Articulate plainly when a use case is a poor fit for AI.

Conduct client and internal demos of Now Assist, AI Agents, AI Control Tower and agentic workflows, clearly explaining how outputs are produced, where guardrails sit, and what the measured impact is

Actively participate in Agile ceremonies, flagging technical and AI‑specific risks (data quality, hallucination exposure, adoption drag, licensing consumption) during planning

Hands‑On Development and Delivery Governance

Build and extend Now Assist skills, AI Agents, agentic workflows, and orchestration logic; author and tune prompts, tool definitions, and agent instructions against defined success criteria

Develop the supporting platform foundation: integrations, Flow Designer and Integration Hub actions, custom tools exposed to agents, Knowledge and catalog data quality, and the taxonomy that AI Search and Now Assist depend on

Configure and tune Predictive Intelligence models, Document and Task Intelligence, Virtual Agent and NLU/Conversational Interfaces, and AI Search relevancy

Extend AI beyond native capabilities via Generative AI Controller, AI Agent Fabric / MCP, and third‑party LLM or agent integrations where the use case warrants it

Establish evaluation discipline: baseline metrics, golden datasets, regression test suites for prompts and skills, A/B and pre/post measurement, and drift monitoring after go‑live

Enforce responsible‑AI guardrails — data handling and PII scoping, role‑based access to AI capabilities, audit and trace requirements, human approval gates, and configuration in AI Control Tower

Safeguard quality through peer reviews, automated tests, and coordinated promotions across dev, test, and prod, including cutover and rollback strategies for AI features

Own defect resolution during UAT and hyper‑care, including model and prompt performance issues, driving root‑cause analysis and continuous tuning

Team Leadership and Mentoring

Coach junior developers on AI fundamentals, prompt and agent design patterns, and the judgment to distinguish a demo from a production‑ready solution

Coordinate daily development tasks, remove roadblocks, and safeguard delivery timelines

Facilitate training sessions and knowledge‑sharing forums that raise AI fluency across the broader delivery team

Innovation and Cross‑Product Leadership

Lead AI delivery across at least one product suite beyond core platform work, understanding the process being augmented well enough to know where AI genuinely helps

Build reusable accelerators — skill libraries, agent patterns, evaluation harnesses, readiness assessments — and drive their adoption across engagements

Track each ServiceNow release for new AI capabilities, evaluate them hands‑on, and advise clients on adoption sequencing and licensing implications

Contribute lessons learned, benchmarks, and technical articles to internal knowledge bases and external community forums

Qualifications

6+ years in the ServiceNow domain, with meaningful recent time spent building AI‑enabled solutions in production 

ServiceNow AI depth – Now Assist, AI Agent Studio and AI Agent Orchestrator, Now Assist Skill Kit, AI Search, Predictive Intelligence, Document/Task Intelligence, Virtual Agent and NLU, AI Control Tower, and Generative AI Controller

Data foundation fluency – Understands that AI outcomes track data quality; comfortable with Workflow Data Fabric, CMDB/CSDM health, knowledge governance, and taxonomy design as prerequisites rather than afterthoughts

Core‑platform expertise – Integrations, Integration Hub, Flow Designer, Service Portal, UI Builder and Workspaces, imports, plus an architecture mindset for performance, scalability, and clean upgrades

Hands‑on coding – Advanced JavaScript and Glide APIs, REST integration design and consumption, auth schemes, and data pipelines; strong vanilla JavaScript fundamentals with testing habits and version‑control discipline

Applied AI craft – Prompt engineering and iteration, retrieval and grounding patterns, tool/function calling, agent decomposition and orchestration, and a working grasp of where LLMs fail and how to contain it

Evaluation and measurement rigor – Defines success metrics before building, tests systematically, and reports honest results including negative ones

Responsible AI judgment – Practical command of data privacy, access control, auditability, bias and hallucination risk, and the governance conversations that come with them

Product depth – Proven leadership in at least one suite beyond core ITSM and Service Portal

Collaborative mentor and lifelong learner – Explains AI concepts simply to non‑technical stakeholders, calibrates expectations against hype, and stays current in a space that changes quarterly

ServiceNow certifications – CSA, CAD, CIS, and AI‑related micro‑certifications are welcome, though demonstrated hands‑on expertise is valued more highly than credentials

Broader tech stack awareness – Familiarity with LLM providers and APIs, vector search and RAG architectures, MCP, cloud platforms, DevOps toolchains, or analytics outside the ServiceNow ecosystem