Staff Enterprise AI Engineer - Agentic Workflows & Productivity

The Ironwood Digital platform enables Communication Service Providers (CSPs) of all sizes to transform and future-proof their businesses. Through real-time data, automation, and actionable insights delivered via Ironwood Digital One — our cloud-first, AI-powered platform — CSPs can simplify operations, collapse cost, and accelerate innovation. Ironwood Digital One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Ironwood Digital mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve. We’re at the forefront of a once in a generational change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences. 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. 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 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). Exp