Agentic AI Specialist

About the Company

The Agentic AI Specialist is the technical authority and master across AI technologies within the AI Factory. You will enable and guide developers (working under the Engineering Manager) to design, build, and operate enterprise-grade AI agents using Azure OpenAI, Copilot Studio, and ServiceNow (including integrations across enterprise knowledge, workflows, and tooling

).

About the

Role

You will define reference architectures, engineering standards, and guardrails; lead complex technical decisions; and ensure delivery is measurable, predictable, secure, reliable, and cost-effective. This role combines hands-on engineering (coding, debugging, reviewing, automation) with technical leadership (roadmaps, tooling, quality systems, mentorship, innova

tion).

Responsi

• bilities
  
  Engineering Strategy, Predictability & Measurability: Deliver the AI Factory engineering strategy so teams can build high-quality agentic solutions with predictable outcomes (capacity planning, delivery metrics, quality gates, cost-to-serve tracking, platf
• orm SLAs).Technical Decisions & Architecture Across Platforms: Make complex technical decisions spanning Azure OpenAI model choices, orchestration patterns, Copilot Studio design, ServiceNow integration patterns, and enterprise arc
• hitecture.Solve Strategic/Complex Problems with Leading-edge Solutions: Resolve complex agentic issues: prompt injection, tool misuse, grounding failures, hallucinations, latency spikes, knowledge freshness, agent memory pitfalls, and secure tool execution
• at scale.Execute & Contribute to the Technical Roadmap: Define and execute a ro
• admap for:Azure OpenAI capability adoption (models, embeddings, content filtering
• , caching)Copilot Studio extensibility (connectors, actions
• , plugins)ServiceNow AI experiences (Virtual Agent/Now Assist patterns, workflow
• triggers)Shared runtime components (tool registry, policy engine, evaluation
• services)Engineering & Operational Excellence (Metrics + Improvement Loops): Establish excellence practices: definition-of-done for AI, release criteria, evaluation regression suites, security reviews, performance baselines, and
• runbooks.Foster Innovation with High Reliability: Create a culture of rapid experimentation with controls: safe sandboxes, feature flags, A/B tests, prompt/version governance, and production readiness c
• hecklists.Hands-on Coding, Testing & Reviews: Write, test, and review code across agent services, middleware, connectors, and orchestration logic; refactor prompt flows and agent policies as
• required.Resolve Escalations (Deep Technical Troubleshooting): Debug and troublesho
• ot across:Agent orchestratio
• n servicesPrompt workflows + evaluatio
• n failuresServiceNow integrati
• ons / APIsCopilot Studio act
• ion chainsIdentity/access issues
• (Entra ID)Observability traces and
• incidentsDrive Technical Vision & Innovation: Contribute to the broader technical direction: new patterns for agent planning/routing, safe tool calling, RAG design, memory strategies, and cross-platform in
• tegration.Tooling & Automation for Developer Productivity: Implement and maintain CI/CD and a
• utomation:Prompt + flow
• versioningAutomated eval
• pipelinesQuality gates (groundedness, relevance, toxici
• ty checks)Automated release
• validationDeveloper templates and s
• caffoldingArchitectures & Standards for Enterprise Scale: Define enterprise stan
• dards for:Agent runt
• ime designRAG architecture (indexing, retrieval,
• citations)Secure tool executio
• n patternsData access
• boundariesTenant-level governance + aud
• it loggingMulti-environment promotion (dev/
• test/prod)Build New Software + Data-driven Improvements (Reduce Tech Debt): Research, design, and build new components, and perform deep analysis of agent telemetry to reduce tech debt and improve reliability, performance, and developer e
• xperience.Mentorship & Technical Coaching: Mentor engineers and squads via design reviews, code reviews, pairing sessions, office hours, and
• playbooks.Knowledge Leadership & Emerging Trends: Continuously research and share best practices in agentic AI, LLMOps, Responsible AI, evaluation techniques, and platform feature

evolution.

Qu

alifications

Technical (Must-have): Strong experience building LLM-powered solutions (RAG, tool calling, prompt engineering, evaluation-driven development). Strong engineering background in Python and/or TypeScript/C# with production-grade quality. Experience with Azure components relevant to AI workloads (APIs, security, monitoring, pipelines). Ability to design scalable enterprise architectures and integra

tion patterns.

Required Skills

Platform (Must-have for this role): Hands-on experience with Azure OpenAI (model usage patterns, embeddings, filtering/safety, cost optimisation). Experience building copilots/assistants using Copilot Studio (connectors/actions/flows). Experience integrating with ServiceNow (workflows, APIs, automation patterns; AI/Virtual Agent exposure is a plus). Engineering Excellence CI/CD, automated testing, code review discipline, documentation playbooks. Observability and production support mindset (SLOs, incident response, postmortems). Security & Governance Mindset Understanding of identity/access controls, secure integration, and AI risk controls. Ability to implement guardrails that prevent unsafe or non-complian

t agent behaviou

r.

Preferred Skills

Desirable Skills (Nice-to-have): LLMOps/AgentOps tooling experience (evaluation pipelines, prompt versioning, tracing frameworks). Knowledge of Microsoft governance/security ecosystem (e.g., Entra ID, Purview concepts, SOC/monitoring practices). Experience implementing knowledge ingestion pipelines and vector stores. Experience with ServiceNow CoE operating models and enterpris

e platform governance.

Pay range

and compensation package

Professional Attributes (Behavioural): Technical authority + coach: raises team capability through mentorship and practical enablement. Structured problem solver: calm during incidents; uses data to dri