Director of AI Engineering

Location : Remote - United States only.
About the role:

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.
Responsibilities
Technical Leadership

- 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

- Claude Code, Cursor, Copilot, or equivalent — measure and improve PR throughput

- Partner with Platform Engineering on infrastructure, data pipelines, and APIs

People & Cross-Functional

- 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

Model & Systems Ownership

- 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

Requirements
Applied AI & Agentic Systems

- Production agentic pipelines using frontier models

- Anthropic SDK · OpenAI SDK · tool use, function calling, multi-agent orchestration

- 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

Model Expertise

- Fine-tuning frontier or open-source models for domain-specific tasks

- LoRA, QLoRA, instruction tuning — not just off-the-shelf API calls

- Training data collection, curation, cleaning, and labeling at scale

- LLM inference and serving optimization

- vLLM, TGI, or equivalent

- Model selection trade-offs — cost, latency, capability, context window

Engineering Depth

- 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

- Celery or similar async task frameworks is a bonus

- 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

Ideal Career Background

- Engineering Man