AI Operations Engineer

Responsibilities

• Drive Adoption of selected agentic AI platform: Serve as the technical SME and primary champion for our rollout, partnering with HR department-by-department to support technical request and access, identify productivity bottlenecks, and implement high-impact AI use cases.
• Establish AI Financial Observability: Define, monitor, and report on enterprise AI costs by department, creating structured dashboards to ensure optimized allocations and fiscal efficiency across our SaaS toolset.
• Govern AI Vendor Risk Assessment: Partner with Information Security and legal teams to review, and assess new AI tools and vendors, validating how to implement connectivity.
• Bridge IT, Security & Engineering Infrastructure: Collaborate with internal infrastructure and security platform engineering teams to translate evolving technical AI guardrails into frictionless business enablement policies.
• Build Repeatable IT Operational Workflows: Architect and document long-term support processes, training modules, and standard operating procedures (SOPs) to transition daily AI ticket triaging, troubleshooting, and license management smoothly to the broader IT support team.

90 Day Plan

• First 30 days: Deep dive into Virta's agentic AI platform and the deployment strategy. Build strong relationships with key partners in IT, GRC, and Foundations Engineering, and shadow current software evaluation queues.
• Day 30-60: Partner on first pilots across the first key business departments, identifying specific workflow opportunities. Deliver the first automated dashboard tracking AI token utilization and cost metrics broken down by department.
• Day 60-90: Formalize the AI vendor risk checklist in partnership with the Security team, deploy self-service AI guides, and document onboarding SOPs so that Tier 1 and Tier 2 IT support staff can autonomously assist users with common generative AI queries.

Must-Haves

• 2+ years of experience in agentic AI, IT operations, and business systems enablement, 4 years technical program management.
• Hands-on technical experience deploying, configuring, or supporting generative AI productivity suites (such as Google Gemini, OpenAI/ChatGPT Enterprise, or Microsoft Copilot) across corporate environments.
• API & Model Orchestration: Hands-on experience integrating with frontier LLMs, multi-agent frameworks, and vector/retrieval systems.
• Software Engineering: Strong fundamentals in general software development with proficiency in languages like Python and C++.
• Systems Integration: Ability to design with Model Context Protocol (MCP) servers and connect AI systems to existing internal legacy databases and web APIs.
• Strong analytical and quantitative skills with experience building dashboard telemetry using APIs (e.g., utilizing Sheets, SQL, Looker, or similar BI tools) to gather info across platforms to track API utilization, software licensing, or operational costs.
• Exceptional agility and communication skills, with a proven ability to demystify complex technical tools for non-technical users and coordinate across IT, Security, and Engineering.
• Successfully designed and implemented repeatable AI-enabled workflows that address team bottlenecks and improve efficiency.
• Can-do attitude with never-ending curiosity to learn.

Core Responsibilities

• Tooling & Infrastructure: Build internal AI platforms, custom copilots, prompt libraries, and AI-agent toolkits designed for specific company workflows.
• Cross-Functional Enablement: Partner with non-technical teams to identify repetitive work and prototype rapid, AI-powered solutions.
• Governance & Standards: Establish the necessary security guardrails, evaluation frameworks, and system guardrails so internal teams can deploy trusted AI safely at scale.
• Training & Mentorship: improve AI literacy in partnership with HR and promote AI adoption across the entire enterprise.