# AI Engineer

**Company:** [Cinder](https://hotfix.jobs/companies/cinder)
**Location:** New York, NY
**Role:** ML Engineering
**Salary:** $200k – $250k/yr
**Experience:** 5+ years
**Skills:** Python, PyTorch, TensorFlow, Keras, LLMs, RAG, MLOps, AWS, Terraform, LangChain, Vector Databases, Prompt Engineering, Fine-Tuning, CI/CD, Model Serving
**Posted:** 2025-12-30

> 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.

## Job Description

## 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**.

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