Machine Learning Operations Engineer - Team Lead

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February 8

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Brixio

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Brixio. com is currently a parked domain hosted by GoDaddy, indicating that it may be available for sale or future development. The website provides a minimal interface with contact information for purchasing the domain and includes advertisements and related links managed by GoDaddy. As of now, Brixio does not appear to operate as an active company or provide any products or services to the public.

11 - 50 employees

💰 Seed Round on 2016-02

📋 Description

• Machine Learning Operations (ML/Ops) Engineer - Team Lead • Location / Work Setup: Remote • About Us: • We are a dynamic and innovative team currently engaged in an exciting Request for Quote (RFQ) process for a client based in Hong Kong. • We are on the lookout for a skilled Machine Learning Operations Engineer, particularly someone experienced in deploying Machine Learning models into pipelines. • This is a fantastic opportunity to be part of a high-impact project where your contributions will shape the future capabilities of our client's ML infrastructure. • Key Responsibilities: • Lead a team of ML/Ops engineers in deploying and managing Machine Learning models. • Build and enhance capabilities to seamlessly integrate ML models into existing pipelines. • Collaborate with cross-functional teams to understand project requirements and ensure successful deployment. • Provide ongoing support to accommodate surges in requirements, focusing on scalability and efficiency. • Utilize Python for scripting and automation tasks. • Work with Cloud platforms, particularly Azure and GCP to optimize machine learning workflows.

🎯 Requirements

• For Team Lead Position: • At least 3 years of experience in ML/Ops or a related field. • Proven leadership skills with the ability to guide and inspire a team. • Excellent communication skills to effectively liaise with clients and internal teams. • For ML/Ops Engineer Position: • Strong experience in deploying Machine Learning models. • Proficiency in Python for scripting and automation. • Hands-on experience with Cloud platforms, particularly Azure and GCP. • Prior experience in addressing scalability and pricing concerns in ML operations.