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 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 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.