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MLOps Engineer (Remote) at NTT DATA

, , Mexico -

Full Time

Start Date

Immediate

Expiry Date

14 Apr, 26

Salary

0.0

Posted On

14 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Machine Learning, Data Science, AWS SageMaker, AWS Cloud Services, MLflow, Kubeflow, GitHub Actions, Typescript, CICD, Kubernetes, Container Orchestration, ML Training, Inference Workflows, Data Processing, APIs

Industry

IT Services and IT Consulting

Description

Build ML Pipelines and deploy models. Define and develop APIs and MCP Serverd Working on projects leveraging your expertise in data science, artificial-intelligence and machine learning. Assist in breaking down complex business problems, developing solutions, and delivering with a high degree of focus on client satisfaction. Conduct market research, develop a point-of-view and communicate effectively back to clients and stakeholders. Bring innovative thinking, resourcefulness leveraging best practices and creativity to achieve successful client outcomes. Establish relationships with our clients at the appropriate levels, gain an understanding of the project work and problems encountered. Work with data sets of varying degrees of size and complexity including both structured and unstructured data. Piping and processing massive data-streams in distributed computing environments. Implement batch and real-time model scoring. Assemble large, complex data sets that meet functional / non-functional business requirements. Apply business knowledge to analyze data, develop reports and solve problems *Perform ad hoc analyses of data depending on business needs. Participate in the analysis and resolution of issues related to information flow and content with data stakeholders. 5 + Years as an ML Ops Engineer with experience in the following: 1. Proficiency in AWS SageMaker and AWS Cloud Services. Experience with ML lifecycle tools (e.g., MLflow, Kubeflow) . Experience in Developing GitHub Actions using Typescript for CICD Experience with Kubernetes for container orchestation. Expertise in ML training and inference workflows.

Responsibilities

The MLOps Engineer will build ML pipelines, deploy models, and define APIs while working on projects that leverage expertise in data science and machine learning. They will assist in breaking down complex business problems and developing solutions with a focus on client satisfaction.