Machine Learning Engineer (Ops)

Accrete.ai

Machine Learning Engineer (Ops)

Edu Angels India Private Limited

Company

Home

Machine Learning Engineer (Ops)

at Accrete.ai

Agency job

via Edu Angels India Private Limited

5 - 14 yrs

₹50L - ₹70L / yr

Mumbai

Skills

Data Science

Machine Learning (ML)

Natural Language Processing (NLP)

Computer Vision

kubeflow

Data Structures

Docker

Kubernetes

PyTorch

TensorFlow

Keras

Amazon Web Services (AWS)

MLOps

Responsibilities:

Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
Design and implement cloud solutions, build MLOps on the cloud (preferably AWS)
Work with workflow orchestration tools like Kubeflow, Airflow, Argo, or similar tools
Data science models testing, validation, and test automation.
Communicate with a team of data scientists, data engineers, and architects, and document the processes.

Eligibility:

Rich hands-on experience in writing object-oriented code using python
Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, and Automated pipelines)
Understanding of Data Structures, Data Systems, and software architecture
Experience in using MLOps frameworks like Kubeflow, MLFlow, and Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc. )

Responsibilities:

Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
Design and implement cloud solutions, build MLOps on the cloud (preferably AWS)
Work with workflow orchestration tools like Kubeflow, Airflow, Argo, or similar tools
Data science models testing, validation, and test automation.
Communicate with a team of data scientists, data engineers, and architects, and document the processes.

Eligibility:

Rich hands-on experience in writing object-oriented code using python
Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, and Automated pipelines)
Understanding of Data Structures, Data Systems, and software architecture
Experience in using MLOps frameworks like Kubeflow, MLFlow, and Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc. )

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