DevOps Engineer

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