Director of AI Engineering
New
United StatesFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience in software engineering, machine learning, or AI engineering, including leadership experience in startup or growth-stage environments.
- Required Skills
- AWSPythonPyTorchTensorflow
Requirements
- 8+ years of experience in software engineering, machine learning, or AI engineering, including leadership experience in startup or growth-stage environments.
- Strong hands-on expertise in building production AI systems, including LLM-based agents, RAG pipelines, and tool-using or multi-agent architectures.
- Deep experience with ML frameworks and infrastructure such as Python, PyTorch/TensorFlow, and LLM ecosystems (e.g., OpenAI, Anthropic APIs).
- Proven ability to design and deploy scalable ML systems, including model evaluation frameworks, observability tooling, and A/B testing methodologies.
- Strong understanding of distributed systems, data pipelines, and cloud infrastructure (preferably AWS).
- Experience with fine-tuning models (LoRA, QLoRA) and managing training data pipelines and labeling workflows.
- Ability to lead engineering teams while remaining deeply technical and actively contributing to code and system design.
- Excellent communication skills with the ability to translate complex AI concepts for both technical and non-technical stakeholders.
Responsibilities
- Lead the design, architecture, and delivery of production AI/ML systems, actively contributing code while guiding team execution across models and infrastructure.
- Own the end-to-end AI roadmap, including prioritization, technical trade-offs, and delivery of agentic workflows and ML-powered features.
- Build and scale advanced AI systems such as LLM-based agents, RAG pipelines, and multi-step reasoning workflows for invoice and vendor intelligence.
- Establish technical standards for model evaluation, observability, reliability, and performance across all AI systems.
- Manage and grow a distributed team of 8–10 ML and Data engineers, fostering strong technical culture and high execution velocity.
- Partner cross-functionally with Product, Platform Engineering, and customer-facing teams to align AI capabilities with business needs and customer feedback.
- Oversee the full ML lifecycle including data collection, training, deployment, monitoring, and continuous optimization of production systems.
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