Director of AI Engineering
United States - Remote
R
USD 198K-250K (estimate) Executive-level Full Time
Tasks
- Architect production AI and ML systems
- Build evals regression suites and human review
- Close feedback loops on model quality
- Communicate AI capabilities and limitations
- Define quality bars for agentic workflows
- Design RAG pipelines chunking embedding retrieval tuning reranking
- Design agent loop planning memory tool execution error recovery
- Design production async distributed systems
- Develop MCP tool accessible server APIs
- Drive agentic coding tool adoption
- Fine tune models using LoRA QLoRA and instruction tuning
- Hire develop and retain engineers
- Implement AI observability tracing cost latency and failure analysis
- Manage distributed data and ML team
- Manage full ML lifecycle experimentation deployment monitoring
- Measure and improve PR throughput
- Optimize LLM inference and serving
- Oversee training data collection curation and labeling
- Own AI roadmap end to end
- Own model performance metrics
- Partner on infrastructure data pipelines and APIs
- Partner with Product on roadmap sequencing
- Run A B testing for model improvements
- Set AI model quality standards
Perks/Benefits
- 401k match
- Annual company retreats
- Medical, dental, vision benefits
- Paid time off
- Promote from within
- Remote work
Skills/Tech-stack
A/B | A/B Testing | API Versioning | AWS | Agent Orchestration | Agentic Workflows | Anthropic SDK | Artificial Intelligence | Async workers | B testing | Celery | Distributed Systems | Embeddings | Evals | Fine Tuning | Function Calling | Instruction Tuning | LLM Inference | LLM serving | Language Models | Large Language Models | LoRA | MCP | Machine Learning | Multi-Agent | Multi-agent orchestration | Observability | OpenAI SDK | PostgreSQL | Python | QLoRA | Queues | RAG | REST APIs | Reranking | Retries | Retrieval tuning | State machines | TGI | Tool use | Tracing | VLLM
Education
N/A
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