Lead structured discovery with practice leaders/experts to understand workflows, data, pain points, and opportunities for AI-driven automation and improved deliverables.
Translate expert needs into clear product requirements, user stories, success metrics, and implementation plans to execute.
Own and maintain an AI capability roadmap focused on AI workflows, agents, and practice-specific tools aligned with BRG strategy and compliance.
Prioritize AI use cases based on impact, feasibility, risk, supportability, and measurable value (efficiency, quality, new offerings).
Drive adoption: build enablement plans, gather feedback, track usage metrics, and iterate to improve sustained value.
Design and ship production AI capabilities such as RAG, prompt/tool patterns, and agent workflows with end-to-end ownership (design → build → test → deploy → monitor).
Implement and improve retrieval quality (chunking, embeddings, hybrid/semantic ranking, prompt design) and establish evaluation approaches (offline/online testing and human-in-the-loop where needed).
Integrate Azure AI services end-to-end (e.g., Azure OpenAI, Azure AI Search, Document Intelligence, orchestration frameworks) into secure and supportable solutions.
Operationalize solutions using CI/CD, telemetry/monitoring, rollout strategies, and reliability targets (SLIs/SLOs) for production readiness.
Provide Tier III support: troubleshoot incidents, perform root cause analysis, implement fixes, and create runbooks for support handoff.

Requirements

Bachelor’s degree in IT, Computer Science, Engineering, Business, or related field (or equivalent experience)
~5+ years of experience in a blend of solution delivery/architecture, AI implementation, product ownership/business analysis, or consulting-style internal enablement.
Strong understanding of modern AI/LLM approaches: prompt engineering, RAG, embeddings, and agents/agentic workflows.
Hands-on ability to build and deliver AI workflows in production and explain tradeoffs to non-technical stakeholders.
Strong communication and stakeholder-management skills; comfort working with senior experts in a professional services environment.
Preferred Azure-focused AI experience (Azure OpenAI, Azure AI Search, Document Intelligence) and/or familiarity with enterprise AI platforms.
Experience with MLOps/DevOps practices (CI/CD, instrumentation, rollout) for LLM apps.
Familiarity with compliance frameworks, AI governance and regulated data considerations.
Preferred Certifications (Examples) Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Solutions Architect Expert, AWS AI Practitioner, AWS Solutions Architect

Benefits

Health insurance
Retirement plans
Flexible work arrangements
Professional development

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI implementationproduct ownershipbusiness analysisprompt engineeringRAGembeddingsagent workflowsMLOpsDevOpsCI/CD

Soft Skills

communicationstakeholder managementproblem-solvingroot cause analysisfeedback gatheringiterationenablement planningprofessional services interactionteam collaborationuser story translation

Certifications

Microsoft Certified: Azure AI Engineer AssociateMicrosoft Certified: Azure Solutions Architect ExpertAWS AI PractitionerAWS Solutions Architect