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CinderCinderNew York, NY

AI Engineer

Builds and deploys production-scale AI/ML systems using LLMs, from fine-tuning and evaluation to low-latency infrastructure. Requires 5+ years experience with PyTorch/TensorFlow, MLOps, AWS, and taking models to production at high-growth startups.

200k – 250k/yr
Hybrid5+ YOEML Engineering

About the role

Responsibilities

  • Own the complete lifecycle of large language model implementation: from data preparation and fine-tuning through rigorous evaluation and production deployment.
  • Develop automated evaluation frameworks that continuously assess model accuracy, identify edge cases, and quantify improvements across iterations.
  • Work directly with product managers and engineers to integrate AI as a core product capability.
  • Shape our AI roadmap by staying current with industry developments, evaluating emerging techniques, and making pragmatic adoption decisions.
  • Design and implement low-latency, high-throughput, cloud-based AI/ML systems capable of handling thousands of requests per second.
  • Build the foundational infrastructure - model serving, monitoring, deployment pipelines, and automated testing frameworks - that enables rapid experimentation and iteration while maintaining production reliability.

Requirements

  • 5-7+ years of engineering experience with demonstrated hands-on knowledge of applying LLMs and agents in industry.
  • Experience at a high-growth startup building machine learning infrastructure from the ground up.
  • Demonstrated ability to take models from research/experimentation through production deployment at scale.
  • Fluency in Python and related AI/ML frameworks (TensorFlow, PyTorch, Keras, etc.).
  • Hands-on experience with LLMs and contemporary AI engineering patterns: RAG architectures, embedding models, vector databases, prompt engineering, and fine-tuning strategies.
  • Curious, systematic, and execution-oriented—you don't wait for perfect requirements and can navigate technical tradeoffs independently.
  • Strong foundation in MLOps: CI/CD for ML, model versioning, monitoring, and observability.
  • Strong technical background in AWS cloud architecture and automated infrastructure provisioning with Terraform.

Nice-to-haves

  • Experience with agentic frameworks like LangChain.

Skills

PythonPyTorchTensorFlowKerasLLMsRAGMLOpsAWSTerraformLangChainVector DatabasesPrompt EngineeringFine-TuningCI/CDModel Serving

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