Machine Learning Engineer

G2i Inc.; United States; On-site; USD 100,000 - 150,000; Full time

Job summary

A technology company is seeking a Machine Learning Engineer to design and deploy models for NLP tasks. You will optimize existing models and build data pipelines, with a focus on fine-tuning large language models. The ideal candidate should have a strong background in machine learning engineering, experience with Python and frameworks like PyTorch and TensorFlow, and familiarity with MLOps tools. This role is based in the United States.

Qualifications

• Experience fine-tuning and deploying LLMs such as OpenAI, Anthropic, Mistral, LLaMA.
• Strong background in machine learning engineering.
• Experience with Python and frameworks like PyTorch, TensorFlow, or Transformers.
• Solid understanding of NLP and model evaluation.
• Experience building end-to-end ML systems.

Responsibilities

• Design, fine-tune, and deploy LLMs for NLP tasks.
• Optimize ML models for performance, cost, and latency.
• Build and maintain data pipelines for training and evaluation.

Skills

Fine-tuning LLMs

Python

Machine learning frameworks

Natural Language Processing

Data preprocessing

Building ML systems

MLOps tools

Tools

PyTorch

TensorFlow

Transformers

AWS

GCP

Azure

Job description

A company is looking for a Machine Learning Engineer experienced in fine-tuning and deploying Large Language Models (LLMs).

Key Responsibilities

• Design, fine-tune, and deploy LLMs for natural language understanding, text generation, and summarization tasks
• Optimize existing ML models for performance, cost, and latency
• Build and maintain robust data pipelines for model training and evaluation

Required Qualifications

• Proven experience fine-tuning and deploying LLMs (OpenAI, Anthropic, Mistral, LLaMA, etc.)
• Strong background in machine learning engineering, with experience in Python and frameworks such as PyTorch, TensorFlow, or Transformers
• Solid understanding of NLP, model evaluation, and data preprocessing
• Experience building end-to-end ML systems, from data ingestion to deployment
• Familiarity with MLOps tools and cloud infrastructure (AWS, GCP, or Azure)