DevOps Engineer

0 - 2 years

4 - 5 Lacs

Posted:6 hours ago| Platform: GlassDoor logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Responsibilities:

  • Collaborate with data scientists, machine learning engineers, and software

engineers to deploy ML models into production environments.

  • Automate model deployment pipelines using CI/CD tools for continuous

integration and delivery of machine learning models.

  • Implement version control for ML models, datasets, and experiments (e.g., using

DVC, MLflow, or similar tools).

  • Develop and maintain infrastructure for model training, validation, and

deployment using cloud platforms (AWS, Azure, GCP).

  • Support scaling and monitoring of deployed ML models, ensuring they meet

performance and reliability requirements in production.

  • Set up and manage containerized environments for model deployment using

tools such as Docker and orchestration platforms like Kubernetes.

  • Develop and maintain monitoring tools to track model performance and data

drift in production environments.

  • Implement and automate workflows for data preprocessing, model training,

testing, and versioning.

  • Troubleshoot and resolve any issues related to model deployment,

performance, or scalability.

  • Collaborate with cross-functional teams to optimize model training pipelines

and ensure seamless integration with production systems.

  • Keep up to date with the latest tools, technologies, and best practices in MLOps

and machine learning.

Requirements:

  • Experience: 0-2 years of experience in MLOps, DevOps, data engineering, or

related fields.

  • Familiarity with machine learning concepts and model deployment processes.
  • Basic experience with cloud platforms such as AWS, Azure, or GCP.
  • Experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) for automating

model deployment pipelines.

  • Exposure to version control tools for ML (e.g., DVC, MLflow, Git).
  • Proficiency in Python, including libraries like Pandas, NumPy, and Scikit-learn

for ML-related tasks.

  • Experience with containerization tools such as Docker and container

orchestration platforms like Kubernetes.

  • Familiarity with model monitoring tools and understanding of concepts like

model drift and performance tracking.

  • Knowledge of SQL and data pipeline technologies to handle and manipulate

large datasets.

  • Strong problem-solving and troubleshooting skills with a focus on scalable and

efficient solutions.

  • Good communication skills and the ability to collaborate effectively with cross-

functional teams.

  • Exposure to ML model deployment frameworks such as TensorFlow

Serving, Seldon, or TorchServe.

  • Familiarity with machine learning pipelines and orchestration tools

like Kubeflow or Apache Airflow.

  • Experience with data storage systems like Amazon S3, Google Cloud Storage, or

databases like PostgreSQL, MongoDB, etc.

  • Knowledge of model versioning and management platforms like MLflow, Weights

& Biases, or ModelDB.

  • Understanding of data security and privacy concerns in deploying machine

learning models.

  • Experience with performance monitoring tools like Prometheus, Grafana,

or Datadog.

Job Types: Full-time, Fresher

Pay: ₹400,000.00 - ₹550,000.00 per year

Benefits:

  • Flexible schedule
  • Health insurance
  • Paid time off

Work Location: In person

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