MLOps Engineer
1 week ago
Company Overview :
We are a cutting-edge technology company specializing in developing and deploying machine learning models at scale. Our team is at the forefront of innovation, leveraging advanced technologies to solve complex problems and drive business impact.
Position :
MLOps Engineer
Experience Level : 5+ Years
Location :
Bangalore (Work from Office )
Job Overview :
We are seeking a highly skilled MLOps Engineer with at least 5 years of experience to join our dynamic team.
The ideal candidate will have a strong background in managing machine learning pipelines on large-scale distributed platforms, ensuring the reliability, scalability, and efficiency of our machine learning systems.
Key Responsibilities :- Design, build, and maintain scalable machine learning infrastructure on distributed platforms .
- Implement and optimize machine learning pipelines for model training, validation, and deployment.
- Collaborate with data scientists, software engineers, and DevOps teams to streamline the endtoend ML lifecycle.
- Monitor and troubleshoot performance issues in production ML systems, ensuring high availability and reliability.
- Develop automation tools and scripts to improve the efficiency of ML operations and deployments.
- Stay updated with the latest trends and best practices in MLOps, integrating new technologies to enhance our
ML :
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- 5+ years of experience in MLOps, with a focus on largescale distributed platforms .
- Proficiency in cloud platforms such as AWS , Azure, or GCP.
- Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or scikitlearn.
- Handson experience with containerization technologies (Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow).
- Solid understanding of DevOps principles and CI/CD pipelines .
- Excellent problemsolving skills and ability to work in a fastpaced, collaborative environment.
- Experience with big data technologies (Hadoop, Spark) and distributed computing .
- Knowledge of machine learning model deployment techniques (e.g., serving frameworks like TensorFlow Serving, MLflow).
- Familiarity with version control systems (Git) and code review processes .
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