AI Operations Lead

We're looking for an AI Operations Lead to accelerate our transition to a native AI business.

This is a hands-on applied AI role, not an AI research role. You'll work across Product, Sales, Customer Success, Marketing, Operations, and Support to identify manual workflows, map messy business processes, build AI-powered automations and copilots, and drive adoption across the teams that use them every day.

You'll be part product operator, part business operations problem-solver, part data analyst, and part AI-native builder. You should be comfortable moving from workflow discovery to prototype to rollout quickly, using tools like ChatGPT, Claude, Cursor, n8n, Zapier, Make, Retool, Notion, SQL, BI tools, APIs, and internal systems.

The goal is not just to increase AI usage. The goal is to help every team build the muscle for using AI instinctively, safely, and effectively to move faster, improve quality, and create more operating leverage.

We're looking for someone with high agency, strong judgment, and a builder mindset, someone who can turn ambiguous operational problems into practical AI workflows that people actually use.

Responsibilities:

• Deploy AI-powered automations, copilots, and agentic workflows across a focused set of non-engineering teams within your first 6–12 months
• Partner with teams across Product, Sales, Customer Success, Marketing, Operations, and Support to deeply understand workflows, bottlenecks, edge cases, and success metrics
• Map existing business processes and identify opportunities where AI can reduce manual work, improve quality, accelerate decision-making, or create better customer experiences
• Build workflows using tools such as ChatGPT, Claude, Cursor, n8n, Zapier, Make, Retool, Notion, SQL, BI tools, APIs, and internal data sources
• Orchestrate integrations between AI tools and core business systems such as CRM, support platforms, marketing automation tools, product analytics, databases, and internal reporting systems
• Measure impact through dashboards, QA checks, usage metrics, time savings, accuracy, adoption, and stakeholder feedback
• Iteratively improve deployed workflows based on user behavior, qualitative feedback, and performance data
• Enable team members to use AI effectively in their daily work through training, documentation, templates, office hours, and hands-on coaching
• Identify and coach internal AI champions who can help scale adoption within their teams
• Create reusable playbooks, prompts, templates, and workflow patterns that make AI adoption repeatable across the company
• Work with Product and Engineering when AI workflows need deeper technical support, productization, or integration into customer-facing systems
• Stay current on emerging AI tools, agentic workflow platforms, and automation patterns, and translate that knowledge into practical business impact

Qualifications:

• 3–6 years of experience in product operations, business operations, revenue operations, strategy, technical program management, solutions, automation, data, or a similar role
• Demonstrated experience using AI to build, ship, automate, or materially improve work in a prior role, project, or team
• Strong fluency with modern AI tools such as ChatGPT, Claude, Cursor, Claude Code, Codex, or similar AI-native development environments
• Technical comfort with low-code/no-code tools, workflow automation platforms, SQL, BI tools, APIs, scripting, or systems integrations
• Strong operational empathy, with the ability to partner closely with business teams, understand their day-to-day work, and build solutions they actually adopt
• Excellent communication skills, including the ability to write crisp documentation, lead engaging trainings, and persuade teams to change how they work
• Analytical and data-driven approach to identifying, prioritizing, measuring, and improving AI-enabled workflows
• High ownership, strong urgency, and comfort operating with autonomy in ambiguous environments
• Strong product judgment, including knowing when to prototype, when to automate, when to involve Engineering, and when to kill an idea
• Bias toward hands-on building over strategy-only work
• Passion for AI, automation, operational excellence, and helping teams build durable leverage

Nice to Have:

• Familiarity with workflows across operating teams such as Sales, Customer Success, Support, Marketing, Finance, RevOps, Product, or People
• Experience with internal enablement, change management, or company-wide tool adoption programs
• Experience in B2B SaaS, MarTech, PropTech, marketplaces, multifamily, or marketing automation
• Experience with Salesforce, HubSpot, Zendesk, Intercom, Pylon, Segment, BigQuery, Snowflake, dbt, Retool, Zapier, Make, n8n, Gumloop, or similar systems
• Experience designing human-in-the-loop workflows, QA processes, escalation paths, or operational guardrails
• Experience with agentic workflows, prompt evaluation, RAG, LLM observability, or AI workflow monitoring

Not For You If:

• You prefer strategy decks and recommendations over hands-on building and rollout
• You want to do AI research or model development rather than applied AI workflow automation
• You are most comfortable managing through others instead of personally building, testing, and iterating solutions
• You are not excited to spend time with business teams understanding messy operational details
• You are uncomfortable with ambiguity, rapid experimentation, and changing tools
• You think success is "shipping an AI demo" rather than driving real adoption and measurable business impact

What Success Looks Like:

• Within 90 days, you have mapped high-friction workflows across several teams and shipped multiple useful AI-powered prototypes
• Within 6 months, you have deployed a portfolio of AI automations, copilots, or agentic workflows that are actively used by teams across the company
• Within 12 months, AI-powered workflows are embedded into team norms, with clear adoption, quality, and impact metrics
• Teams are saving meaningful time, improving consistency, and moving faster because of workflows you helped build
• The business has a repeatable operating model for identifying, building, launching, and improving AI workflows across the company