Senior Agentic Insights Engineer

NVIDIA; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

NVIDIA IT is transforming leadership decisions with an integrated insights layer. We seek a Senior Agentic Insights Engineer to build and operate agentic infrastructure that generates trusted, actionable recommendations for CIOs and executives.

You will collaborate with Planning & Portfolio Management and Enterprise Data Warehousing to standardize metrics and deliver timely, executive-facing insights across platforms like Slack and Teams.

Qualifications

• 12+ years in data/analytics engineering, BI, or applied AI/ML with a related degree.
• Hands-on production experience building agentic AI workflows and MCP integrations.
• Familiarity with lakehouse platforms (Databricks) and governed data catalogs.
• Ability to validate AI outputs against underlying data before sharing with leadership.
• Experience turning data into practical, executive-ready recommendations.

Responsibilities

• Develop conversational analytics agents tied to IT portfolio data.
• Architect scheduled-push delivery for persona-based insights in Slack/Teams.
• Define and implement agent-output validation to catch incorrect results.
• Transform grounded outputs into clear operational recommendations for executives.
• Manage CIO insights digest and Planning & Portfolio Management reporting cadence.
• Collaborate to align metric definitions and data certification across teams.

Skills

Agentic AI

Tool calling

LLM orchestration

MCP integration

Data storytelling

Executive comms

Slack/Teams

Autonomy

Production-grade

Education

Bachelor's degree in CS/DS/Engineering

Tools

Databricks

Slack

Microsoft Teams

Job description

NVIDIA powers the AI revolution - and inside NVIDIA IT, we're transforming how leadership makes decisions. Our AI Insights & Intelligence team is replacing fragmented dashboards and static reports with a managed insights layer. This layer delivers clear recommendations directly to IT leadership and the CIO.

We’re looking for a Senior Agentic Insights Engineer to be the hands‑on technical builder behind this transformation. In this role, you'll develop and operate the agentic infrastructure that generates insights. You will also apply your judgment to ensure every recommendation is accurate before it reaches a leader's inbox. What makes this role outstanding? It blends production‑level agentic AI engineering with direct responsibility for the operational recommendations that leadership implements within the same week. You'll collaborate closely with our Planning & Portfolio Management and Enterprise Data Warehousing teams, using governed data to develop the insight and delivery layer on top. If you're energized by building AI systems that compose real executive decisions — not just dashboards that sit on a shelf — this is your role.

What you’ll be doing:

1. Develop conversational analytics agents tailored to IT portfolio and program data. Link these agents to enterprise data platforms via the Model Context Protocol (MCP) or equivalent open agent‑tooling standards for reliable data retrieval.
2. Architect scheduled‑push delivery mechanics for persona‑based insights in Slack and Microsoft Teams, demonstrating native platform capabilities where available and building custom integrations where they fall short.
3. Define and implement the agent‑output validation layer. It catches incorrect, ungrounded, or hallucinated results before they reach leadership. This shifts the team's reporting from manual dashboard authorship to agentic tooling.
4. Transform grounded agent output into clear operational recommendations. Explain what changed, why it matters, and the specific action a portfolio owner or executive should take. Deliver results in days, not sprint cycles.
5. Manage the CIO executive insights digest and Planning & Portfolio Management reporting rhythm, linking each "at risk" status to a specific recommendation instead of merely describing it.
6. Partner with Planning & Portfolio Management to keep aligned with their schedule, differentiating proposals that are feasible this week from those that should be assigned elsewhere.
7. Apply data storytelling and UX expertise to frame each insight for its specific audience, ensuring leadership can act on it within minutes.
8. Collaborate with the Enterprise Data Warehousing, AI Engineering, and Reporting & Dashboards teams to ensure uniformity in metric definitions, data certification, and delivery throughout the organization.

What we need to see:

1. 12+ years of experience in data/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.
2. Hands‑on experience building agentic AI workflows in production is required. This includes tool‑calling, multi‑step LLM orchestration, and MCP or equivalent experience integrations. You should understand failure modes like hallucinated tool calls, runaway loops, and non‑deterministic output.
3. Direct experience with a modern lakehouse platform (such as Databricks), including catalog‑governed tables and conversational analytics tooling.
4. Strong analytical judgment demonstrated by the ability to validate or challenge AI‑generated answers against underlying data before they ship to a business audience.
5. Demonstrated success in converting data into practical business or operational suggestions instead of only dashboards. Skilled at writing for a CIO or VP audience, ensuring the message is clear in under three minutes.
6. Experience building or integrating scheduled and event‑driven delivery mechanisms into Slack, Microsoft Teams, or equivalent enterprise collaboration tools.
7. Comfort operating as the primary technical builder on a small team, moving from prototype to production with a high degree of autonomy.

Ways to stand out from the crowd:

1. Experience replacing a manual or contractor‑dependent BI/reporting function with an agentic or automated alternative.
2. Familiarity with enterprise program and portfolio management tools like Jira‑based platforms is required. The role involves building automated, executive‑facing reports with push/pull and agentic delivery methods.
3. Applied use of prescriptive analytics techniques - trend detection, anomaly flagging, or forecasting - in service of operational decision‑making.
4. Prior partnership with C‑level or VP‑level collaborators on recurring executive reporting.
5. Contributions to an internal agent or skills community of practice, or experience building reusable agent capabilities for others to extend.

Your base salary will be determined based on your location, experience, and the pay o