Applied AI Lead
Summary
Hands-on applied AI lead at Instinct, a raw pet food brand, owning internal AI agents, automations and tools end to end — mainly partnering with Finance on reporting automation and analytics. Core stack: Python, LLM APIs/agents (tool use, MCP), workflow automation, APIs and SQL, with human-in-the-loop guardrails. Remote, with occasional travel to the St. Louis HQ.
- Own agents, builds, and orchestration across Instinct priorities, from scoping through production handoff.
- Design, build, and maintain internal AI tools, automations, and integrations (Python scripts, APIs, LLM agents with tool use, workflow automation) that solve business challenges.
- Build tools that others can run and improve without the builder: usable by non-developer teammates and maintainable by Instinct developers.
- Serve as the primary build partner to the CFO on reporting automation, close and forecast support, and decision-ready analytics.
- Partner with FP&A and other decision owners to turn recurring manual reporting and analysis into automated, trustworthy workflows.
- Establish a working operating rhythm with Finance leadership to manage the priority queue.
- Maintain a short, ranked list of AI use cases by dollar impact and feasibility, and be willing to stop low-value work before it consumes engineering time.
- Run fast, low-cost experiments to test emerging AI capabilities against real business challenges before committing budget or scaling.
- Scout new tools, models, and techniques and bring forward the ones with real potential ahead of demand.
- Deliver production tools the business relies on, not demos or pilots that fade after the first presentation.
- Build on trusted data in partnership with Data Architecture, Master Data, and Data Control.
- Perform data-quality audits on source feeds prior to automation and flag issues back to data owners.
- Partner with IT and data teams on integrations, access, and security for AI workloads.
- Define safe-use guardrails with Data Hub leadership: what AI may answer unsupervised and what requires human review.
- Document what is safe to trust and make the governed path the easy path for Finance and business users.
- Monitor AI tool usage, cost, and performance, with automated reporting and KPI tracking.
- Technically review and validate work from external vendors and developers for quality, security, and fit before anything goes live.
- Drive adoption of AI tools through onboarding materials, working examples, and reusable playbooks and templates.
- Act as the day-to-day technical reference point for Finance and business users on approved AI tools.
- Communicate trade-offs, limitations, and results clearly to executives and non-technical audiences.
- Builder mindset with a preference for shipping a working solution over writing a recommendation
- Strong business acumen with the ability to connect AI work to financial and commercial outcomes
- Plain communicator who can explain trade-offs without hype, to all audiences
- Sound judgment on risk, data quality, and where a human should own the call
- Self-directed and comfortable owning priorities end to end in a remote environment
- Curious, with a bias toward fast experimentation and continuous improvement
- Ability to translate complex data and technology into actionable business insights
- A true collaborator who builds trust cross-functionally
- 4-8+ years building applied AI, ML, or automation solutions in a business setting (finance, CPG, operations, or similar)
- Background in CPG, pet food, food & beverage, or a similarly complex operating environment is a plus
- Bachelor's degree in a related field or equivalent practical experience
- Hands-on proficiency in Python (or equivalent), LLM APIs and agents (including tool use and MCP), workflow automation, and APIs; able to ship, not only advise
- Demonstrated experience partnering with Finance or similar decision owners on reporting, forecasting, or analytics automation
- Experience with SQL, data-quality auditing, and building on governed data sources
- Experience defining safe-use guardrails and human-in-the-loop controls for AI workflows
- Experience technically evaluating or overseeing the work of vendors, contractors, or consultants
- Track record of prioritizing use cases by dollar impact and feasibility and delivering them to production
- Familiarity with AI governance and data privacy practices is a plus