- Design, develop, and maintain data science pipelines for model training, evaluation, and deployment.
- Manage and optimize infrastructure resources (e.g., cloud services, containers) to support model deployment and inference
- Collaborate with data scientists, software engineers, and DevOps teams to deploy machine learning models using best practices in MLOps.
- Automate endtoend ML workflows, including data preprocessing, model training, evaluation, and deployment, using tools like Kubeflow or Apache Airflow
- Implement CI/CD pipelines for automated model deployment, testing, and monitoring.
- Utilize Kafka and other messaging systems for realtime data processing and streaming analytics.
- Optimize distributed computing infrastructure for scalability, performance, and cost efficiency.
- Manage GitHub repositories for version control and collaboration on machine learning projects.
- Utilize various data connectors and integration tools to access and process data from different sources.
- Develop and maintain documentation for data science pipelines, infrastructure, and processes.
- Stay up to date on emerging technologies and best practices in machine learning operations and data engineering.
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Machine Learning Operations Engineer - Gurgaon/Gurugram/Delhi NCR, India - Conneqtcorp
3 weeks ago
![Default job background](https://contents.bebee.com/public/img/bg-user-ex-1.jpg)
Description
Role - Machine Learning Operations EngineerSkills - MLOps, Deployment of Models
Experience - 5+ Years
Location - Gurugram/Delhi NCR
Notice Period - Max 30 days
Job Description :
We are looking for an experienced MLOPs Engineer with expertise in Spark/PySpark, MLOps/LLMops/DLOps, CI/CD, Kafka, Python, distributed computing, GitHub, data pipelines, cloud hosting, Azure services, Microsoft services, various data connectors, and more.
This role will involve designing, implementing, and optimizing data science pipelines, deploying machine learning models, and ensuring smooth operation in production environments.
Responsibilities :