Director of AI Engineering
New
O
OttimateFinance, Hospitality
Remote - United States only.Full-TimeDirector
Salary200,000 - 225,000 USD per year
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Job Details
- Required Skills
- AWSPostgreSQLPython
Requirements
- Production agentic pipelines using frontier models
- Experience with Anthropic SDK
- Experience with 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)
- Fine-tuning frontier or open-source models for domain-specific tasks
- Experience with LoRA, QLoRA, instruction tuning
- Training data collection, curation, cleaning, and labeling at scale
- LLM inference and serving optimization (vLLM, TGI, or equivalent)
- Hands-on Python programming
- 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 expertise
- Engineering Manager ready for director-level ownership
- Experience leading technical teams at a startup or growth-stage company
- Hands-on contributor who has also managed small high performance teams
- Comfortable owning outcomes
Responsibilities
- Architect and ship production AI/ML systems
- 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
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