AI Product and Solutions Engineer

BRGPittsburgh, PA
1d$130,000 - $190,000Remote

About The Position

BRG is seeking a full-time AI Product & Solutions Engineer to drive firm-wide adoption of secure and governed AI by combining product ownership and practice discovery with hands-on engineering and production delivery. This role partners directly with practice leaders and experts to identify high-value opportunities, define success metrics, and deliver scalable AI-enabled solutions, especially those involving LLMs, RAG pipelines, and agentic workflows, built primarily on Azure. The role also serves as a key liaison across BRG’s expert communities, internal IT teams, and vendors to ensure solutions are secure, compliant, operationally supportable, and cost-effective, with clear documentation and measurable business impact.

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.

Nice To Haves

  • 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.
  • Microsoft Certified: Azure AI Engineer Associate
  • Microsoft Certified: Azure Solutions Architect Expert
  • AWS AI Practitioner
  • AWS Solutions Architect

Responsibilities

  • 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 agentic 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.
  • Ensure solutions comply with BRG security, privacy, and regulatory requirements; implement governance patterns (RBAC/Entra ID, Key Vault/secrets, content safety/guardrails, private networking where needed).
  • Create and maintain architecture and integration documentation that supports auditability, reuse, and long-term support.
  • Monitor utilization and optimize cost/performance (model choice, throughput strategy, search tier sizing) with reporting on value delivered.
  • Manage end-to-end efforts for onboarding/integrating AI-related SaaS or services (requirements, vendor selection, implementation, integration, training, ongoing support).
  • Collaborate with internal IT, business partners, and vendors while managing multiple initiatives and maintaining strong customer relationships.
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