

A2C
Artificial Intelligence Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an Artificial Intelligence Engineer with a contract length of "unknown," offering a pay rate of "unknown." Key skills required include Python, cloud-native development, Docker, Kubernetes, and experience with Large Language Models.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
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ποΈ - Date
October 21, 2025
π - Duration
Unknown
-
ποΈ - Location
Unknown
-
π - Contract
Unknown
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π - Security
Unknown
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π - Location detailed
New York, United States
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π§ - Skills detailed
#AI (Artificial Intelligence) #Data Science #API (Application Programming Interface) #Model Deployment #Databases #Cloud #Documentation #Docker #Deployment #Python #Kubernetes
Role description
Role Focus:
Weβre seeking an AI Engineer to design, deploy, and manage Large Language Models (LLMs) at scale. This role centers on building and optimizing LLM training, hosting, and serving pipelines β ensuring models are performant, reliable, and production-ready.
What Youβll Do:
β’ Build, fine-tune, and optimize LLMs for real-world applications.
β’ Design and manage model deployment pipelines with API-based serving, GPU orchestration, and scaling strategies.
β’ Monitor and enhance model performance, latency, and reliability in production environments.
β’ Work with vector databases, embeddings, and tokenization techniques for inference optimization.
What You Bring:
β’ Strong proficiency in Python and modern software engineering best practices.
β’ Experience in cloud-native development, containerization, and orchestration (Docker, Kubernetes).
β’ Hands-on experience in model training, evaluation, and optimization workflows.
β’ Comfortable in fast-paced, experimental environments β rapid prototyping and iteration.
β’ Excellent communication and documentation skills; able to collaborate across data science, engineering, and product teams.
Role Focus:
Weβre seeking an AI Engineer to design, deploy, and manage Large Language Models (LLMs) at scale. This role centers on building and optimizing LLM training, hosting, and serving pipelines β ensuring models are performant, reliable, and production-ready.
What Youβll Do:
β’ Build, fine-tune, and optimize LLMs for real-world applications.
β’ Design and manage model deployment pipelines with API-based serving, GPU orchestration, and scaling strategies.
β’ Monitor and enhance model performance, latency, and reliability in production environments.
β’ Work with vector databases, embeddings, and tokenization techniques for inference optimization.
What You Bring:
β’ Strong proficiency in Python and modern software engineering best practices.
β’ Experience in cloud-native development, containerization, and orchestration (Docker, Kubernetes).
β’ Hands-on experience in model training, evaluation, and optimization workflows.
β’ Comfortable in fast-paced, experimental environments β rapid prototyping and iteration.
β’ Excellent communication and documentation skills; able to collaborate across data science, engineering, and product teams.