AI Engineer; Org Wide

Responsibilities

• Own design, build, deployment, and operation of AI solutions from intake through production retirement.
• Maintain audit‑readiness, security posture, and data integrity in every implementation.
• Document architecture decisions, data lineage, evaluation results, and operational runbooks.
• Translate business problems into solution architectures grounded in data quality, retrieval design, and governance.
• Evaluate when to apply Copilot Studio versus Azure AI Foundry versus traditional automation, with rationale tied to need and risk.
• Surface data foundation gaps before agent build, naming dependencies and risks proactively.
• Deliver solutions with rigorous validation, monitoring, rollback paths, and source citation.
• Treat every production AI capability as accountable software, not an experiment.
• Maintain high standards for grounding quality, prompt control, and output reliability.
• Collaborate with client compliance, project management, data, and business unit stakeholders to align solutions with operational reality.
• Translate technical constraints into plain language for non‑technical stakeholders, including client executive leadership.
• Identify when to push back on a use case and when to find a path forward.
• Work effectively with LC Plus Technology peers, client partner functions, external consulting partners, and vendor representatives.
• Support onboarding of new use cases through client intake processes.
• Mentor LC Plus Technology peers and client counterparts on Microsoft AI build practices.
• Demonstrate commitment to the LC Plus Technology mission and to the client‑first ethic in technical work.
• Uphold the consistency and accountability standard expected of all solutions that serve clients and the populations they serve.
• Operate with full HIPAA and BAA discipline within regulated client environments.

Under the supervision of the IT Operations Manager, the AI Engineer is a hands‑on builder responsible for designing, deploying, and operating artificial intelligence solutions across LC Plus Technology's client portfolio. The role builds retrieval‑grounded agents in Microsoft Copilot Studio and Azure AI Foundry, designs the data ingestion and integration patterns that make those agents reliable, and partners with client compliance, project management, and data analytics functions to ensure every solution respects the regulated environments in which the clients of LC Plus Technology operate.

The engineer joins a small, focused technical team and is the voice on AI build feasibility across client conversations. The role mentors peers on Microsoft AI build practices, operates LC Plus Technology's AI Solution Inventory, and stays current on Microsoft's AI platform evolution so it can be translated into client roadmap implications. Initial focus for this role is anchored on managed care client work, with the scope expanding across the client portfolio over time.

• Own design, build, deployment, and operation of AI solutions from intake through production retirement.
• Maintain audit‑readiness, security posture, and data integrity in every implementation.
• Document architecture decisions, data lineage, evaluation results, and operational runbooks.
• Translate business problems into solution architectures grounded in data quality, retrieval design, and governance.
• Evaluate when to apply Copilot Studio versus Azure AI Foundry versus traditional automation, with rationale tied to need and risk.
• Surface data foundation gaps before agent build, naming dependencies and risks proactively.
• Deliver solutions with rigorous validation, monitoring, rollback paths, and source citation.
• Treat every production AI capability as accountable software, not an experiment.
• Maintain high standards for grounding quality, prompt control, and output reliability.
• Collaborate with client compliance, project management, data, and business unit stakeholders to align solutions with operational reality.
• Translate technical constraints into plain language for non‑technical stakeholders, including client executive leadership.
• Identify when to push back on a use case and when to find a path forward.
• Work effectively with LC…