Senior Agentic Insights Engineer

NVIDIA•Santa Clara, CA

7d•$184,000 - $287,500

About The Position

NVIDIA IT’s AI Insights & Intelligence team is building a managed insights layer to replace fragmented dashboards and static reports with clear recommendations for IT leadership and the CIO. The Senior Agentic Insights Engineer is the hands-on technical builder responsible for developing and operating the agentic infrastructure that generates those insights, and for validating recommendations before they reach leaders. The role combines production agentic AI engineering with responsibility for operational recommendations that leadership can implement quickly. The engineer will work with Planning & Portfolio Management and Enterprise Data Warehousing teams, using governed data to build the insight and delivery layer. The work includes conversational analytics agents, scheduled insight delivery, agent-output validation, executive reporting, and translating grounded results into concise, actionable recommendations.

Requirements

12+ years of experience in data or analytics engineering, BI development, or applied AI/ML engineering, with a Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
Hands-on experience building production agentic AI workflows, including tool-calling, multi-step LLM orchestration, and MCP or equivalent integrations.
Understanding of agentic AI failure modes, including hallucinated tool calls, runaway loops, and non-deterministic output.
Direct experience with a modern lakehouse platform such as Databricks, including catalog-governed tables and conversational analytics tooling.
Strong analytical judgment and the ability to validate or challenge AI-generated answers against underlying data before they reach a business audience.
Demonstrated success turning data into practical business or operational recommendations rather than only dashboards.
Ability to write clearly for CIO- or VP-level audiences, communicating the message in under three minutes.
Experience building or integrating scheduled and event-driven delivery mechanisms into Slack, Microsoft Teams, or equivalent enterprise collaboration tools.
Ability to operate as the primary technical builder on a small team and move from prototype to production with a high degree of autonomy.

Nice To Haves

Experience replacing a manual or contractor-dependent BI/reporting function with an agentic or automated alternative.
Familiarity with enterprise program and portfolio management tools such as Jira-based platforms.
Applied use of prescriptive analytics techniques, such as trend detection, anomaly flagging, or forecasting, for operational decision-making.
Prior partnership with C-level or VP-level collaborators on recurring executive reporting.
Contributions to an internal agent or skills community of practice, or experience building reusable agent capabilities for others to extend.

Responsibilities

Develop conversational analytics agents for IT portfolio and program data, connecting them to enterprise data platforms through MCP or equivalent open agent-tooling standards.
Architect scheduled-push delivery of persona-based insights in Slack and Microsoft Teams, using native platform capabilities where available and building custom integrations as needed.
Define and implement a validation layer that catches incorrect, ungrounded, or hallucinated agent results before they reach leadership.
Turn grounded agent output into clear operational recommendations that explain what changed, why it matters, and what action a portfolio owner or executive should take.
Manage the CIO executive insights digest and Planning & Portfolio Management reporting rhythm, linking each at-risk status to a specific recommendation.
Coordinate with Planning & Portfolio Management to align with its schedule and distinguish proposals feasible for the current week from those that should be assigned elsewhere.
Apply data storytelling and UX expertise to tailor insights to each audience so leadership can act on them quickly.
Collaborate with Enterprise Data Warehousing, AI Engineering, and Reporting & Dashboards teams to maintain consistency in metric definitions, data certification, and delivery.