Remote Contract ML Engineer — NLP & LLM

Tech Holding; Oregon; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Tech Holding is seeking a hands-on Machine Learning Engineer to design, train, and deploy NLP/LLM-powered models for an AI-driven risk intelligence platform. You will work with data engineers and product stakeholders in a remote setting, delivering scalable services with monitoring and evaluation frameworks.

Experience with RAG, embeddings, and vector search is preferred. You will contribute to production-grade ML pipelines across classification, NER, topic modeling, and semantic similarity,

Qualifications

• 4+ years of professional experience building and deploying machine learning models in production.

Responsibilities

• Design, train, and deploy ML models for text classification, entity extraction, clustering, and narrative and topic detection
• Build and improve LLM-powered features, including prompt design, retrieval-augmented generation (RAG), and agentic workflows
• Develop embedding and semantic-search pipelines to group related content and surface emerging threats
• Productionize models as scalable, monitored services with clear latency, cost, and accuracy targets
• Build evaluation frameworks and datasets to measure model quality, bias, and drift over time
• Work with data engineering to improve ingestion, labeling, and feature pipelines for high-volume, noisy, multilingual text data
• Monitor and retrain models in production, and debug quality issues end to end
• Document approaches and share trade-offs clearly with technical and non-technical stakeholders

Skills

Python

PyTorch

TensorFlow

Hugging Face Transformers

NLP

LLMs

RAG

Vector search

AWS

GCP

Azure

Docker

CI/CD

Model evaluation

Communication

Tools

Docker

CI/CD

AWS

GCP

Azure

Job description

Tech Holding is seeking a hands-on Machine Learning Engineer to design, train, and deploy NLP/LLM-powered models for an AI-driven risk intelligence platform. You will work with data engineers and product stakeholders in a remote setting, delivering scalable services with monitoring and evaluation frameworks.

Experience with RAG, embeddings, and vector search is preferred. You will contribute to production-grade ML pipelines across classification, NER, topic modeling, and semantic similarity,