Senior Applied AI Engineer - Remote, Production ML Leader

Bright Vision Technologies; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

Bright Vision Technologies seeks a highly experienced Applied AI Engineer to design, develop, and deploy enterprise-scale AI solutions. This is a fully remote, direct-hire role within the Continental United States, offering a challenging, high-impact environment.

Responsibilities include building end-to-end ML pipelines, tuning LLMs, and leading technical initiatives across cloud environments. Ideal candidates have 10+ years in AI/ML, strong Python and ML framework expertise, and a proven track

Qualifications

• 10+ years of professional experience in AI, ML, and DL.
• Expert-level Python with PyTorch, JAX, or TensorFlow.
• Proven experience deploying LLMs, NLP, CV in production.
• Strong leadership, collaboration, and architecture skills.

Responsibilities

• Design, develop, and deploy enterprise AI solutions at scale.
• Architect training, inference, and MLOps pipelines.
• Fine-tune and optimize LLMs, NLP, and multimodal models.
• Lead cross-functional teams and set best practices for Responsible AI.
• Evaluate new AI technologies and maintain secure, scalable systems.

Skills

Python

PyTorch

JAX

LLMs

NLP

Computer Vision

MLOps

AWS/Azure/GCP

Leadership

Retrieval-Augmented Generation

Education

Bachelor's or Master's in CS/AI/DS

PhD preferred

Tools

Kubernetes

Docker

Pinecone

Weaviate

Milvus

FAISS

LangChain

LlamaIndex

LangGraph

Ray

Job description

Bright Vision Technologies seeks a highly experienced Applied AI Engineer to design, develop, and deploy enterprise-scale AI solutions. This is a fully remote, direct-hire role within the Continental United States, offering a challenging, high-impact environment.

Responsibilities include building end-to-end ML pipelines, tuning LLMs, and leading technical initiatives across cloud environments. Ideal candidates have 10+ years in AI/ML, strong Python and ML framework expertise, and a proven track