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Open nowFirst seen 15 hours ago

Sr. MLOps Engineer

PradeepIT Consulting Services Pvt Ltd2,005 open roles

Where
Bengaluru, KA, India, Remote
Work mode
Remote
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Your applicationOpen nowSr. MLOps EngineerPradeepIT Consulting Services Pvt Ltd · Bengaluru, KA, India, Remote

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: first seen 15 hours ago

The posting

-------------------------------------------------------------------------------------------

| Skills | Year of experience | Remarks | Weightage |

-------------------------------------------------------------------------------------------

| MLOps & Python | 3+ | Mandatory | 60% |

| Cloud | 3+ | Mandatory | 20% |

| Machine Learning | 2+ | Good to have | 10% |

| DevOps | | Good to have | 10% |

------------------------------------------------------------------------------------------

Job Description

Total Exp in years - 4 7

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 cloud (preferably Azure)
  • Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
  • Data science models testing, validation and tests automation.
  • Communicate with a team of data scientists, data engineers and architect, document the processes.

Mandatory Skills:

  • 4 7 years of experience in MLOps and Data engineering
  • Rich hands-on experience of 3+ years 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, Automated pipelines)
  • Understanding of Data Structures, Data Systems and software architecture
  • Experience in using MLOps frameworks like Kubeflow, MLFlow, Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
  • Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.
  • Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
  • Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with proven experience of Terraform/Spark
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