via Career pages·Today
MLE/MLOps consultant
Infosys
Full-timeOn-site
Location:Bangalore, IndiaType:Full-timePosted:Today
We are looking for smart, self-driven, high energy people with top notch communication skills, intellectual curiosity and passion for technology in Machine Learning Space. Our analysts have a blend of in-depth domain expertise in one or more areas (in Retail and CPG Domain), strong business acumen and excellent soft skills & Exposure to Technology.
Technical Skills: Must Have Skills:
- MLOps Implementation and Support Experience
- Python Programming - Expert and Experienced - 4 -5 years
- DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum
- Hands-On MS Azure Cloud knowledge
- Operations Experience if any
- List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup
- Well verse in coding standards (flake8 etc)
- Automation, Technology and Process Improvement for the deployed projects
- Agile trained to manage team effort and track through JIRA
Nice to have Skills:
- Understanding of any one of domain (Eg: Retail, Supply chain, Logistics, Manufacturing).
Soft Skills:
- Strong verbal and written communication skills with the ability to work well in a team.
- Strong customer focus, ownership, urgency, and drive.
- Ability to handle multiple, competing priorities in a fast-paced environment.
- Work well with the team members to maintain high credibility.
Work Experience:
- 3-5 years of experience in Machine Learning, Azure and MLOps.
- Understand the requirements from the business and translate it into an appropriate technical requirement.
- Responsible for successful delivery of MLOps solutions and services in client consulting environments;
- Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client.
- Assist clients with operationalization metrics to track performance of ML Models
- Help team with ML Pipelines from creation to execution
- Guide team to debug on issues with pipeline failures
- Understand and take requirements on Operationalization of ML Models from Data Scientist
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