MLOps Engineer

3 weeks ago

Bengaluru, Karnataka, India DoublU Pty Ltd Full time

About the Role :

We are seeking a highly skilled and motivated MLOps Engineer to join our growing Data Science team.

As an MLOps Engineer, you will play a crucial role in bridging the gap between data science and IT operations by designing, building, and maintaining robust and scalable machine learning pipelines.

You will ensure the smooth and efficient deployment, monitoring, and maintenance of machine learning models in production environments.

Key Responsibilities :

- Oversee the deployment of ML models from development to production environments using tools like Kubernetes, Docker, and serverless platforms.

- Design and implement CI/CD pipelines for ML model training, testing, and deployment.

- Automate model deployment processes to improve efficiency and reduce manual effort.

- Continuously monitor the performance of deployed models in production.

- Identify and address issues such as data drift, model degradation, and concept drift.

- Implement robust monitoring and alerting systems to detect anomalies and proactively address issues.

- Contribute to the development and maintenance of an MLOps platform, including tools for model versioning, experiment tracking, and artifact management.

- Integrate MLOps tools and technologies into the existing data science workflow.

- Collaborate closely with data scientists, data engineers, and software engineers to ensure smooth model development and deployment.

- Communicate effectively with stakeholders on technical and non-technical topics related to MLOps.

- Participate in team meetings, code reviews, and knowledge sharing sessions.

- Stay up-to-date with the latest advancements in MLOps technologies and best practices.

- Explore and evaluate new tools and technologies to improve MLOps processes.

Qualifications :

Essential :

- 4-9 years of experience in software engineering or a related field with at least 2 years of experience in MLOps.

- Strong proficiency in Python and experience with machine learning frameworks (e., TensorFlow, PyTorch, scikit-learn).

- Experience with containerization technologies (Docker, Kubernetes).

- Experience with CI/CD pipelines and tools (e., Jenkins, GitLab CI/CD).

- Experience with cloud platforms (AWS, Azure, GCP) and their machine learning services.

- Strong understanding of software engineering principles and best practices.

- Excellent problem-solving, analytical, and debugging skills.

- Strong communication and interpersonal skills.

- Bachelor's degree in Computer Science, Engineering, or a related field.

Preferred :

- Experience with MLOps tools such as MLflow, Kubeflow, or Argo Workflows.

- Experience with data streaming and messaging systems (e., Kafka, Kinesis)

(ref:hirist.tech)

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