Position : MLOps Engineer.
Experience : 7+ Years.
Location : Noida / Bengaluru.
Work Mode : Hybrid (Work From Office).
Job Description :
We are seeking a highly skilled and experienced MLOps Engineer with a strong background in machine learning infrastructure, DevOps practices, and cloud-native technologies.
The ideal candidate will have over 7 years of relevant experience and a deep understanding of the end-to-end machine learning lifecycle, including data pipelines, model training, deployment, and monitoring.
Key Responsibilities :
- Design, build, and manage scalable MLOps pipelines and infrastructure to support machine learning model development and deployment.
- Collaborate with Data Scientists, Engineers, and Product teams to ensure seamless integration of ML models into production systems.
- Develop and maintain CI / CD workflows for ML applications using tools such as Jenkins, GitLab CI, etc.
- Manage containerized applications using Docker and orchestrate deployments with Kubernetes.
- Automate model serving, monitoring, and performance tracking in production environments.
- Troubleshoot and resolve complex production issues related to ML infrastructure.
Qualifications :
Bachelors or Masters degree in Computer Science, Engineering, or a related field.Proven experience working with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.Proficiency in Python, Shell scripting, and familiarity with ML / DL frameworks such as TensorFlow, PyTorch, and Keras.Experience with deep learning techniques and generative AI frameworks is highly desirable.Strong understanding of DevOps principles and tools used in continuous integration and continuous delivery (CI / CD).Prior experience in roles such as Platform Engineer, ML DevOps Engineer, or Data Engineer is preferred.Strong software engineering skills, especially in multi-language and complex system environments.Excellent analytical, problem-solving, and troubleshooting skills.Strong interpersonal and communication skills with the ability to work collaboratively across teams.(ref : hirist.tech)