Senior AI Engineer - LLMs & Multimodal Systems (Hybrid)

Dataminr; Arlington (VA); Híbrido; USD 155.000 - 228.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible work arrangements

Global offices

Generous PTO and sick leave

Descripción de la vacante

Dataminr in the United States is seeking a Senior AI Engineer to advance LLMs and Agentic AI, collaborating with researchers to train, adapt, and deploy across predictive, generative, and agentic AI. Hybrid role with two in-office days weekly in New York City or Arlington, VA.

You will work on multimodal data and scale models, contributing to end-to-end LLMOps from research to productization in a fast-growing AI platform that processes billions of inputs daily.

Formación

• MS in Computer Science or equivalent experience.
• 5+ years as a Research or ML Engineer.
• MLOps/LLMOps experience with PyTorch, Transformers and LLMs.
• Experience with NLP, CV, knowledge representation and generative AI.
• Expertise in Python.
• Experience with containers, Kubernetes, AWS, Databricks and LangGraph/LangFuse.

Responsabilidades

• Deploy, build, adapt, optimize LLM models at very large scale.
• Own the design and implementation of API services, libraries, and container strategies for LLMOps.
• Optimize DL/LLM training and inference performance on specialized hardware.
• Drive full lifecycle of AI problems from research to productization.

Conocimientos

Python

MLOps/LLMOps

LLMs/Transformers

PyTorch

Kubernetes

AWS

LangGraph

LangFuse

Vector stores

LLM deployment

Educación

M.S. in Computer Science

Herramientas

Containers

Kubernetes

AWS

Databricks

LangGraph

LangFuse

Vector stores

Descripción del empleo

Dataminr in the United States is seeking a Senior AI Engineer to advance LLMs and Agentic AI, collaborating with researchers to train, adapt, and deploy across predictive, generative, and agentic AI. Hybrid role with two in-office days weekly in New York City or Arlington, VA.

You will work on multimodal data and scale models, contributing to end-to-end LLMOps from research to productization in a fast-growing AI platform that processes billions of inputs daily.