Machine Learning Engineer

About Quantzig : .
Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India.
we have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, Machine Learning, and data engineering solutions implementation for prudent decision making.
We are a rapidly growing organization that is built and operated by high-performance champions If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one were looking for.
The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth story.
Please Find The Below
Experience : 4 to 5 Yrs.
Notice Period : Immediate Joiner.
Role : Machine Learning Engineer.
Responsibilities

• 
  Design and maintain engineering infrastructure to support enterprise machine learning systems in Azure cloud.
• Support model development, with following industry best practices on auditability, versioning, and data security.
• Facilitate development and deployment of proof-of-concept machine learning solutions.
• Collaborate with Data Scientists to take offline machine learning model solutions and deploy them into production system.
• Very good hands-on experience in deploying end-to-end ML solutions as a ML DevOps Engineer, or Data Engineer.
• Manage stakeholders and drive timely execution of projects using agile methodologies.
• Build reliable and scalable solutions in a fast-paced work environment.
• Knowledge sharing with other team members on technical skills and best practices.
  

Desired Skillset

• University degree in Computer Sciences or a similar field of studies.
• At least 3 to 5 years of relevant experience in deploying ML solutions into production systems.
• Exposure to machine learning frameworks and best practices.
• Deep knowledge in Python, T SQL, database systems.
• Ability to translate business needs into technical requirements.
• Familiarity with data-oriented workflow orchestration frameworks such as AirFlow, KubeFlow etc.
• Very high data affinity and strong analytical skills
  

(ref:hirist.tech)