Senior Machine Learning Engineer

Tenjumps Softech

Chennai District

On-site

INR 1,200,000 - 1,800,000

Full time

6 days ago
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Job summary

Tenjumps Softech in Chennai is seeking a skilled ML Ops Senior Engineer with a strong data engineering background to lead deployment, monitoring, and management of ML models in production.

The role requires collaboration with data scientists, software engineers, and DevOps to optimize the ML pipeline, ensure scalability, and high availability of ML applications.

Responsibilities

  • Lead the design, development, and implementation of ML Ops solutions to deploy machine learning models into production environments efficiently, leveraging data engineering best practices.
  • Integrate data pipelines with ML models, including model versioning, data lineage, monitoring, hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines.
  • Implement and maintain infrastructure.

Job description

Job Description:

Overview:

We are seeking a highly skilled and experienced ML Ops Senior Engineer with a strong background in data engineering to join our dynamic team.

The ideal candidate will be responsible for leading and implementing the deployment, monitoring, and management of machine learning models in production environments, while also possessing expertise in data engineering principles.

This role requires a deep understanding of both machine learning and data engineering concepts, as well as proficiency in software engineering and DevOps practices.

The ML Ops Senior Engineer will collaborate closely with data scientists, software engineers, and DevOps professionals to optimise the end-to-end ML pipeline, ensure model scalability, and maintain high availability of ML applications.

Responsibilities:
  • Lead the design, development, and implementation of ML Ops solutions to deploy machine learning models into production environments efficiently, leveraging data engineering best practices.
  • Collaborate with data engineers, data scientists, and software engineers to integrate data pipelines with ML models (including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines) with best practices, ensuring data quality, reliability, and scalability.
  • Implement and maintain infrastructure.
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