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ML-Ops Engineer

3 months ago

Pune, India Volkswagen Group Technology Solutions India Full time

Role and Responsibilities:


Role:

We are seeking a highly skilled MLOps Engineer to join our growing team and play a critical role in building and deploying our next-generation Artificial Intelligence and Machine Learning (AIML) solutions on the cloud. You will be responsible for bridging the gap between development and operations, ensuring a smooth transition of machine learning models from experimentation to production. With your expertise in MLOps practices and cloud technologies, you will build and maintain robust pipelines for training, testing, deploying, and monitoring AIML models at scale.

Responsibilities:

- Design, develop, and implement automated MLOps pipelines for the entire AIML lifecycle (training, testing, deployment, monitoring) on cloud platforms (e.g., AWS, Azure, GCP).

- Utilize CI/CD tools and practices to automate the build, test, and deployment of machine learning models.

- Integrate MLOps pipelines with version control systems and data management solutions.

- Develop and implement unit and integration tests for MLOps pipelines.

- Configure and manage cloud infrastructure for efficient training and deployment of AIML models.

- Monitor model performance in production, identify potential issues, and implement corrective actions.

- Collaborate with data scientists, machine learning engineers, and DevOps teams to ensure seamless integration and operation of AIML solutions.

- Stay up-to-date on the latest advancements in MLOps tools and cloud technologies relevant to AIML deployments.

- Continuously improve MLOps processes and optimize the performance and efficiency of AIML pipelines. Successful deployment and rollout of AIML products


Technical Competencies:

- Minimum 5 years of experience in software engineering or a related field.

- Strong understanding of Machine Learning concepts and the ML lifecycle.

- Experience with MLOps tools and frameworks (e.g., Kubeflow, MLflow, Metaflow).

- Experience with monitoring tools and techniques for ML models (e.g., Prometheus, Grafana).

- Experience with specific AIML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

- Proficiency in cloud platforms (AWS, Azure, GCP) and their machine learning services (e.g., SageMaker, Azure Machine Learning, AI Platform).

- Experience with CI/CD tools and methodologies (e.g., Git, Jenkins, GitLab CI/CD).

- Experience with scripting languages (e.g., Python, Bash) and familiarity with containerization technologies (Docker, Kubernetes) is a plus.

- Excellent problem-solving and analytical skills.

- Strong communication and collaboration skills to work effectively with cross-functional teams.

- Passion for building robust and scalable solutions for deploying AIML models.