Job Opportunities API

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 E