Machine Learning Ops Engineer

2 days ago

Bengaluru, India Zinnov Full time

Job Title: Machine Learning Ops Engineer

Job Level: Mid-Level

Job Location: Bangalore, India

Key Responsibilities:

- Design, implement and maintain ML pipelines for model training, validation, and deployment

- Automate model deployment processes using CI/CD pipelines and containerization technologies

- Monitor model performance, data drift, and system health in production environments

- Collaborate with data scientists to operationalize machine learning models and algorithms

- Implement version control for models, datasets, and ML experiments using MLOps tools

- Optimize ML infrastructure for scalability, reliability, and cost-effectiveness

- Troubleshoot and resolve issues related to model deployment and production systems

- Maintain documentation for ML workflows, deployment processes, and system architecture

- This position may require availability outside of standard business hours as part of a rotational on-call schedule.

What You'll Need to Be Successful (Required Skills):

- 2-4 years of experience in software development, DevOps, or data engineering

- Proficiency in Python, SQL, and at least one ML framework such as TensorFlow, PyTorch, Scikit-learn

- Experience with containerization (Docker) and orchestration tools (Kubernetes)

- Knowledge of cloud platforms such as AWS, Azure, GCP and their ML services

- Understanding of CI/CD pipelines, version control (Git), and infrastructure as code

- Familiarity with monitoring tools and logging frameworks for production systems

- Experience with data pipeline tools such as Apache Airflow, Kubeflow, or similar

- Strong problem-solving skills and ability to work in fast-paced, collaborative environments.

Education/ Certifications:

- Bachelor's degree in computer science, Information Management or related field.

Preferred Skills:

- Experience with MLOps platforms such as MLflow, Weights & Biases, Neptune

- Knowledge of streaming data processing such as Kafka, Kinesis

- Familiarity with infrastructure monitoring tools such as Prometheus, Grafana

- Understanding of model interpretability and explainability techniques

- Experience with feature stores and data versioning tools

- Certification in cloud platforms such as AWS ML, Azure AI, GCP ML

(ref:hirist.tech)

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