Senior AI/LLM Engineer — Production-Scale NLP

Matlen Silver; Washington (District of Columbia); In loco; USD 103.000 - 110.000; Tempo pieno

Vantaggi offerti da questo lavoro

401(k)

Vision insurance

Medical insurance

Descrizione del lavoro

Matlen Silver is seeking a Senior AI/LLM Engineer to design, build, and optimize advanced AI solutions with a strong focus on Large Language Models. You will work with data scientists and engineers to deliver cutting-edge capabilities, from prototype to production.

The role requires deep ML expertise, hands-on experience with LLMs, and deployment in production environments using Kubernetes, Docker, and cloud platforms such as AWS/GCP/Azure.

Competenze

• Bachelor’s or Master’s degree in CS/AI/ML; PhD preferred.
• 7+ years AI/ML experience; 2+ years with LLMs.
• Strong Python and ML framework skills (PyTorch, TensorFlow, Transformers).
• Deep knowledge of LLM fine-tuning, prompt engineering, RAG, embeddings, vector DBs, and distributed training.
• Production ML deployment experience with Kubernetes, Docker, AWS/GCP/Azure.
• Experience with data pipelines, MLOps, and ML CI/CD.
• Strong problem solving and collaboration.

Mansioni

• Design, build, and optimize AI solutions with LLMs.
• Collaborate with data scientists and engineers to deliver AI capabilities.
• Scale models to production; monitor performance and reliability.

Conoscenze

Python

PyTorch

TensorFlow

HuggingFace Transformers

Kubernetes

Docker

AWS

GCP

Azure

MLOps

CI/CD

LLMs

Formazione

Bachelor’s or Master’s degree in CS/AI/ML

PhD preferred

Strumenti

Docker

Kubernetes

AWS

GCP

Azure

CI/CD tools

Descrizione del lavoro

Matlen Silver is seeking a Senior AI/LLM Engineer to design, build, and optimize advanced AI solutions with a strong focus on Large Language Models. You will work with data scientists and engineers to deliver cutting-edge capabilities, from prototype to production.

The role requires deep ML expertise, hands-on experience with LLMs, and deployment in production environments using Kubernetes, Docker, and cloud platforms such as AWS/GCP/Azure.