Director of AI Engineering

Ottimate

3d • Remote

About The Position

Ottimate is building the AI-native future of accounts payable. Our platform processes millions of invoices across hundreds of enterprise customers, powered by a suite of ML models and agentic workflows. As Director of AI Engineering, you will own the full AI and ML layer of our product — from invoice understanding and vendor intelligence to our conversational AP Copilot and the next generation of autonomous AP agents.
This is a hands-on leadership role. You will spend at least half your time writing code, architecting systems, and driving technical decisions alongside your team. You will also set the AI roadmap, partner cross-functionally with Product, Data, and Platform Engineering, and manage a distributed team of 8–10 engineers across Data and ML.
We are looking for a senior technical manager or director — ideally someone who has thrived at a smaller company and is ready for a career step up into broader ownership. If you are energized by shipping real AI products, working with noisy real-world financial data, and building the systems that will define how enterprises automate AP, this role is for you.

Requirements

• Senior technical manager or director
• Thrived at a smaller company and is ready for a career step up into broader ownership
• Energized by shipping real AI products, working with noisy real-world financial data, and building the systems that will define how enterprises automate AP
• Engineering Manager ready for director-level ownership
• Has led technical teams at a startup or growth-stage company — knows how to move fast
• Hands-on contributor who has also managed small high performance teams.
• Comfortable owning outcomes
• Hands-on Python — comfortable writing, reviewing, and shipping production code
• PostgreSQL — schema design, query optimization, indexing strategies
• Distributed systems — async workers, queues, retries, state machines
• Public-facing API design — REST, versioning, developer experience
• MCP server development — tool-accessible APIs for AI agent integration
• AWS or cloud infrastructure — enough to own AI workload deployments

Nice To Haves

• Celery or similar async task frameworks is a bonus
• Finance and/or AP domain — invoice workflows, GL coding, vendor management
• Hospitality — high-volume, multi-location AP operations
• Noisy, unstructured text data — OCR outputs, inconsistent supplier formats, entity resolution at scale

Responsibilities

• Architect and ship production AI/ML systems — you write code, not just review it
• Own the AI roadmap end-to-end: prioritization, trade-offs, delivery
• Set technical standards for model quality, evals, observability, and reliability
• Drive adoption of agentic coding tools to multiply team velocity
• Partner with Platform Engineering on infrastructure, data pipelines, and APIs
• Manage a distributed team of 8–10 engineers across Data and ML disciplines
• Hire, develop, and retain engineers at all levels; build a high-trust remote culture
• Partner with Product on roadmap sequencing and scope trade-offs
• Work directly with customer-facing teams to close feedback loops on model quality
• Communicate AI capabilities and limitations clearly to non-technical stakeholders
• Own model performance metrics and drive continuous improvement pipelines
• Build and maintain evals frameworks — regression suites, human review, A/B testing
• Oversee training data collection, curation, and labeling operations
• Manage the full ML lifecycle: experimentation, deployment, monitoring, iteration
• Define and enforce quality bars for agentic workflows entering production
• Production agentic pipelines using frontier models
• Reliable agent loop design — planning, memory, tool execution, error recovery
• RAG pipeline design — chunking, embedding models, retrieval tuning, reranking
• Evals frameworks built from scratch — correctness, regression, semantic similarity
• Observability for production AI — tracing, cost tracking, latency, failure analysis
• Fine-tuning frontier or open-source models for domain-specific tasks
• Training data collection, curation, cleaning, and labeling at scale
• LLM inference and serving optimization
• Model selection trade-offs — cost, latency, capability, context window
• Hands-on Python — comfortable writing, reviewing, and shipping production code
• PostgreSQL — schema design, query optimization, indexing strategies
• Distributed systems — async workers, queues, retries, state machines
• Public-facing API design — REST, versioning, developer experience
• MCP server development — tool-accessible APIs for AI agent integration
• AWS or cloud infrastructure — enough to own AI workload deployments

Benefits

• 200,000-225,000 + 15% Annual Bonus
• Competitive salary based on skills & experience.
• Medical, Dental, Vision and other Company-Subsidized Benefits for you and your family.
• Employer sponsored 401(k) with company match.
• Paid Time Off (and the encouragement to use it).
• Annual company retreats.

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: No Education Listed