ServiceNow Senior Technical Consultant - AI

AHEAD

1d•$140,000 - $170,000•Onsite

About The Position

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation. At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD. We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived. We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD. As a Senior Technical Consultant focused on AI capabilities, you will own the end-to-end build of AI-enabled solutions on the Now Platform — Now Assist skills and AI Agents through predictive models, AI Search, and the data foundations that make them work. You will guide development activities, mentor technical consultants and junior developers, and partner with Principal consultants and architects to shape complex agentic solutions. Beyond core platform development, you will lead AI enablement across at least one additional product suite (ITSM, ITOM, ITAM, SecOps, IRM, CSM, HRSD, SPM, or ESM) and translate ambiguous business outcomes into secure, governed, measurable AI capabilities. Your depth in both platform engineering and applied AI will influence how our clients adopt agentic workflows and how they realize value from them.

Requirements

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

Responsibilities

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
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
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
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