Staff Enterprise AI Engineer - Agentic Workflows & Productivity
Role details
Job location
Tech stack
Job description
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.
This is a remote-based position located in the United States or Canada. Please note that as part of the recruitment and hiring process, there is an in-person meeting that will take place.
What You'll Do
Enterprise platform rollout & enablement
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Own the architecture and rollout of the enterprise AI platform across the product organization.
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Build and operate cost-effective infrastructure: model garden setup, budget controls and cost monitoring, model tiering, and fallback open-source models.
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Stand up and harden platform foundations - identity federation/SSO, IAM, tenant isolation, security and compliance guardrails, and observability.
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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.
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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
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Lead the design and build of agent workflows across the PDLC:
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Ideation: requirement ideation and generation, design specification generation.
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Development: coding assistants, source-code management.
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Deployment: CI/CD, deployment, and monitoring.
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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.
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Establish evaluation, quality, and monitoring practices for agent workflows from sandbox to production.
Cross-platform interoperability
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Build agents that interoperate with agents on other enterprise AI platforms, including cross-platform contracts, schema/intent mapping, and cross-perimeter authentication.
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Represent the product organization technically when working with other business teams on cross-platform agent designs.
Across all tracks - leadership & hands-on
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Own the technical strategy, standards, patterns, and guardrails for agentic workflow development, RAG, security, and governance.
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Lead and mentor a team across cloud and enterprise AI tracks; raise the bar through code review, pairing, and design reviews.
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Steer external delivery partners - scope work, review deliverables, and hold them to quality and timeline.
Requirements
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10+ years of software engineering experience, with 4+ years in a technical lead or staff-level role.
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Strong, current hands-on coding ability - you still build and ship. Proficiency in Python (and comfort across at least one other modern language).
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Experience designing and operating production AI/ML or LLM-based systems: agents, RAG, prompt/eval pipelines, or similar.
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Deep familiarity with Google Vertex AI / Gemini and the surrounding services (IAM, networking, observability, cost management).
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Experience building developer-facing platforms or internal tooling, and driving adoption with training and support.
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Experience integrating systems via APIs and connectors; comfort with authentication and identity federation (OAuth, SSO, workload identity).
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A track record of leading technical initiatives across teams and influencing without authority.
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Strong communication skills - able to work directly with both engineers and non-technical business stakeholders.
Preferred Qualifications
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Hands-on with Gemini Enterprise, Vertex AI, NotebookLM, or comparable enterprise AI tooling.
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Experience building agentic systems and orchestration (agent development kits, A2A protocols, MCP, tool/function calling).
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Experience with LLM gateways and routing (e.g., LiteLLM), model cost optimization, and multi-model fallback strategies.
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Experience managing or working alongside systems integrators / delivery partners.
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Familiarity with the modern PDLC toolchain (Jira/Confluence, Figma, GitHub, CI/CD) and AI coding assistants.
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Experience with AI governance, guardrails, data privacy, and compliance in an enterprise setting.