Uber Hiring Computer Science Graduates, Postgraduates

Overview:

Uber is hiring an experienced MLOps Engineer at their Gurgaon location. This role is specifically envisioned to help scale, productionalize and industrialize our projects using the latest standard MLOps principles.

The complete details of this job are as follows:

Roles and Responsibilities:

Basic Qualifications :

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 3+ years of hands-on experience in MLOps and Machine Learning Engineering.
• 4+ years of hands-on experience in data science, analytics or related fields
• Strong proficiency in programming languages such as Python, with experience in libraries like TensorFlow, PyTorch, and scikit-learn.
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring.

• Solid understanding of cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Experience in developing and deploying RESTful APIs and microservices for model serving.
• Proficiency in data wrangling, feature engineering, and exploratory data analysis.
• Familiarity with databases and query languages for data extraction and transformation.
• Experience with version control systems like Git and collaborative coding environments.
• Excellent problem-solving skills and the ability to troubleshoot complex technical issues.
• Strong communication skills, both written and verbal, with the ability to explain technical concepts to non-technical stakeholders.

Eligibility Criteria:

• Advanced degree (Master’s or PhD) in a relevant field.
• Familiarity with Big Data technologies such as Hadoop and Spark.
• Contributions to open-source MLOps or Machine Learning Engineering projects.
• Published research papers or filed patents in the field of machine learning.
• Experience with data visualization and reporting tools.
• Understanding of software development methodologies and Agile practices.

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