ServiceNow Engineer-LLMOps & GenAI Integration
Skills
About This Role
Key Responsibilities
- Engineer and operationalize Generative AI workflows within ServiceNow Now Assist and AI Control Tower, leveraging proprietary LLM models and Azure OpenAI endpoints.
- Build and govern RAG (Retrieval-Augmented Generation) pipelines that ground ServiceNow virtual agents in internal knowledge bases, SOPs, and technical documentation corpora.
- Manage the full LLMOps lifecycle — prompt versioning, model drift monitoring, hallucination guardrails, and cost optimization — within the ServiceNow AI Control Tower observability layer.
- Develop custom Now Assist skill extensions using GenAI Studio, integrating with internal APIs and ERP/EAM systems.
- Establish AI quality gates within ServiceNow AI Control Tower to monitor LLM response accuracy, latency SLAs, and safety guardrails across all deployed AI agents.
- Design and implement vector embedding pipelines for 40+ years of petro-technical documentation, enabling semantic search within ServiceNow.
- Produce MLOps dashboards within AI Control Tower tracking token consumption, model performance KPIs, and ROI metrics across all AI initiatives.
Mandatory Qualifications
- 8+ years of software engineering with minimum 4 years on ServiceNow platform and 2+ years in LLMOps or MLOps.
- Certified ServiceNow Developer (CSD) with AI/Now Assist specialization or equivalent demonstrable expertise.
- Hands-on production experience building RAG pipelines using LangChain, LlamaIndex, or equivalent frameworks.
- Proficiency with vector databases (Pinecone, Weaviate, Chroma, or Azure AI Search).
- Experience managing LLM deployments on Azure OpenAI Service or AWS Bedrock within enterprise IT environments.
- Strong Python, REST API, and ServiceNow Scripting (GlideScript/Rhino) expertise.
- Bachelor's or Master's degree in Computer Science, AI Engineering, or Data Science.
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