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 a seasoned technical lead to 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 You'll 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 of agent workflows across 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 ship infrastructure but drive 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 for agentic workflow development, 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 / Gemini and 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.