Staff Enterprise AI Engineer Agentic Workflows & Productivity

About the Role:

We are standing up an enterprise AI capability across our product organization and we are looking for aseasoned technical leadto drive it end to end. This is a hands-on role: you will set the technical direction architecture and standards mentor engineers and steer external delivery partners-while still writing code building reference implementations and personally unblocking the hardest problems.

What Youll Do:

Enterprise Platform Rollout & Enablement

• Own the architecture and rollout of the enterprise AI platform across the product organization.

• Build and operate cost-effective infrastructure: model garden setup budget controls and cost monitoring model tiering and fallback open-source models.

• Stand up and harden platform foundations-identity federation/SSO IAM tenant isolation security and compliance guardrails and observability.

• Enable users to adopt off-the-shelf capabilities (e.g. NotebookLM enterprise search code assistants) and provide the training and ongoing support that drives real adoption.

• Design and deliver a self-service agent factory-patterns templates and in-IDE guardrails-so teams can build and maintain their own agents safely.

Product Development Lifecycle Agents

• Lead the design and build ofagent workflowsacross the PDLC:

• Ideation:requirement ideation and generation design specification generation.

• Development:coding assistants source-code management.

• Deployment:CI/CD deployment and monitoring.

• Partner directly with product engineering and other business teams to understand their requirements and help build out their use-cases-not just shipinfrastructure butdrive measurable productivity gains.

• Establish evaluation quality and monitoring practices for agent workflows from sandbox to production.

Cross-Platform Interoperability

• Build agents that interoperate with agents on other enterprise AI platforms including cross-platform contracts schema/intent mapping and cross-perimeter authentication.

• Represent the product organization technically when working with other business teams on cross-platform agent designs.

Across All Tracks-Leadership & Hands-on

• Own the technical strategy standards patterns and guardrails foragentic workflowdevelopment RAG security and governance.

• Lead and mentor a team across cloud and enterprise AI tracks; raise the bar through code review pairing and design reviews.

• Steer external delivery partners-scope work review deliverables and hold them to quality and timeline.

• Stay hands-on: build reference agents RAG pipelines integrations and MCP connectors and debug the hardest pieces (including non-deterministic retrieval/generation behavior).

Required Qualifications:

• 10 years of software engineering experience with 4 years in a technical lead or staff-level role.

• Strong current hands-on coding ability-you still build and ship. Proficiency in Python (and comfort across at least one other modern language).

• Experience designing and operating production AI/ML or LLM-based systems: agents RAG prompt/eval pipelines or similar.

• Deep familiarity with Google Vertex AI / Geminiand the surrounding services (IAM networking observability cost management).

• Experience building developer-facing platforms or internal tooling and driving adoption with training and support.

• Experience integrating systems via APIs and connectors; comfort with authentication and identity federation (OAuth SSO workload identity).

• A track record of leading technical initiatives across teams and influencing without authority.

• Strong communication skills-able to work directly with both engineers and non-technical business stakeholders.

Preferred Qualifications:

• Hands-on with Gemini Enterprise Vertex AI NotebookLM or comparable enterprise AI tooling.

• Experience building agentic systems and orchestration (agent development kits A2A protocols MCP tool/function calling).

• Experience with LLM gateways and routing (e.g. LiteLLM) model cost optimization and multi-model fallback strategies.

• Experience managing or working alongside systems integrators / delivery partners.

• Familiarity with the modern PDLC toolchain (Jira/Confluence Figma GitHub CI/CD) and AI coding assistants.

• Experience with AI governance guardrails data privacy and compliance in an enterprise setting.