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# Applied Machine Learning Engineer

- **Company:** PERMITFLOW INC.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Mining, Elasticsearch, Python (Programming Language), Machine Learning, OpenAI, Tensorflow, Search Technologies, Systems Integration, Unstructured Data, Pinecone, Pytorch, Large Language Models, Generative AI, Indexer, Backend, Agentic-AI, HuggingFace, Weaviate, Machine Learning Operations, FAISS, Claude, GPT
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_bf6caf18eb76ddb0a97d962e55d9d70a

## About the Role

* 5+ years of experience in machine learning engineering, with production ML experience
 * Deep expertise in NLP and LLMs (OpenAI GPT, Claude, Hugging Face models)
 * Experience building retrieval and vector search systems (e.g., FAISS, Elasticsearch, Pinecone, Weaviate)
 * Proficiency in Python and ML frameworks like PyTorch or TensorFlow
 * Strong track record of deploying and scaling ML systems with measurable business impact
 * Experience with cloud ML infrastructure (AWS, GCP, or Azure)
 * Strong system design and architectural thinking, with a bias toward shipping and iterating quickly
 * Comfort operating in fast-moving startup environments with high ownership and autonomy

## Description

* Design, implement, and optimize LLM-powered models for document processing, data extraction, and permit workflow automation
 * Develop retrieval-augmented generation (RAG) pipelines and search/retrieval systems for jurisdictional and regulatory data
 * Rapidly prototype, fine-tune, and evaluate pre-trained models for real-world NLP tasks like classification, entity recognition, and summarization
 * Build scalable ML infrastructure and backend services, integrating models into production systems that power AI agents
 * Work with large structured and unstructured datasets to improve indexing, retrieval, and contextual accuracy
 * Own the full ML lifecycle: experimentation, deployment, monitoring, evaluation, and iteration
 * Balance ML, retrieval, and rule-based approaches to ship reliable, maintainable, and high-impact AI features
 * Collaborate with engineering, product, and domain experts to shape ML-powered solutions for complex pre-construction challenges