AI Product and Solutions Engineer

📋 Description

• • BRG Communications LLC is seeking a dynamic and innovative AI Product and Solutions Engineer to spearhead the firm-wide adoption of secure and governed Artificial Intelligence. This pivotal role bridges the gap between product ownership, practice discovery, and hands-on engineering, ensuring that cutting-edge AI solutions, particularly those leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG) pipelines, and agentic workflows, are effectively developed and implemented, primarily within the Azure ecosystem.
• • The AI Product and Solutions Engineer will act as a crucial liaison, fostering collaboration between BRG's diverse expert communities, internal IT departments, and external vendors. This ensures that all AI solutions are not only technically sound but also adhere to stringent security, compliance, and operational supportability standards, while remaining cost-effective and delivering measurable business impact.
• • The role is designed with a balanced focus, allocating approximately 50% of the time to product-centric activities and 50% to engineering and delivery. This dual focus allows for a comprehensive approach to AI implementation, from initial ideation and strategic planning to robust production deployment and ongoing maintenance.
• • In the Product/Discovery/Adoption facet (approx. 50%), responsibilities include leading structured discovery sessions with practice leaders and subject matter experts to deeply understand their workflows, data landscapes, existing pain points, and identify high-value opportunities for AI-driven automation and enhancement of deliverables. This involves translating complex expert needs into clear, actionable product requirements, user stories, well-defined success metrics, and comprehensive implementation plans.
• • The role also entails owning and actively maintaining an AI capability roadmap, strategically aligned with BRG's overarching business objectives and compliance mandates. This roadmap will prioritize the development and deployment of AI workflows, agents, and practice-specific tools. Prioritization will be driven by a rigorous assessment of potential impact, technical feasibility, associated risks, long-term supportability, and the quantifiable value (e.g., efficiency gains, quality improvements, new service offerings) that each AI initiative can deliver.
• • A significant aspect of this product-focused work involves driving user adoption. This includes developing comprehensive enablement plans, actively soliciting and incorporating user feedback, meticulously tracking usage metrics, and iteratively refining solutions to ensure sustained value and user satisfaction.
• • The Engineering/Delivery/Operations facet (approx. 50%) demands hands-on expertise in designing, building, testing, deploying, and monitoring production-ready AI capabilities. This includes developing sophisticated RAG pipelines, implementing advanced prompt and tool patterns, and architecting agentic workflows with end-to-end ownership from conception through to operationalization.
• • A key responsibility is the continuous improvement of retrieval quality within RAG systems. This involves optimizing chunking strategies, selecting appropriate embedding models, implementing hybrid or semantic ranking techniques, and refining prompt design. Establishing robust evaluation approaches, encompassing both offline testing and online A/B testing, along with human-in-the-loop validation where necessary, is critical for ensuring solution efficacy.
• • The engineer will be responsible for integrating various Azure AI services, such as Azure OpenAI, Azure AI Search, and Document Intelligence, into secure, scalable, and supportable solutions. This requires a deep understanding of the Azure ecosystem and best practices for enterprise AI deployment.
• • Operationalizing these AI solutions is paramount. This involves leveraging CI/CD pipelines for automated deployments, implementing comprehensive telemetry and monitoring systems, defining and executing effective rollout strategies, and establishing clear reliability targets (SLIs/SLOs) to ensure production readiness and stability.
• • Furthermore, the role includes providing Tier III support, which involves diagnosing and resolving complex incidents, conducting thorough root cause analyses, implementing necessary fixes, and developing detailed runbooks to facilitate smooth handoffs to support teams.
• • Governance, Security, Compliance, and Cost Discipline are embedded across all responsibilities. The AI Product and Solutions Engineer must ensure all developed solutions strictly adhere to BRG's security, privacy, and regulatory requirements. This includes implementing robust governance patterns such as Role-Based Access Control (RBAC) via Entra ID, secure secrets management using Key Vault, content safety features and guardrails, and implementing private networking where necessary.
• • Creating and maintaining comprehensive architecture and integration documentation is essential for auditability, promoting reuse of components, and ensuring long-term maintainability and supportability of AI solutions.
• • Continuous monitoring of AI service utilization and proactive optimization of cost and performance are key. This involves strategic model selection, efficient throughput management, appropriate sizing of search tiers, and regular reporting on the delivered business value against costs.
• • The role also encompasses Cross-Functional Delivery and Vendor/SaaS Enablement. This involves managing the end-to-end process for onboarding and integrating AI-related Software-as-a-Service (SaaS) offerings or third-party services, from defining requirements and vendor selection through to implementation, integration, user training, and ongoing support.
• • Effective collaboration with internal IT teams, business partners, and external vendors is crucial, requiring the ability to manage multiple concurrent initiatives while cultivating and maintaining strong, positive customer relationships throughout the engagement lifecycle.