Pune, Maharashtra, India
Information Technology
Full-Time
Google
Overview
Minimum qualifications:
Responsibilities
- Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.
- 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
- 5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning or natural language processing.
- Experience various database technologies, ETL/ELT processes, and data warehousing/lake concepts.
- Experience in designing and managing data solutions on cloud platforms (e.g., Google Cloud Platform).
- Experience with Angular, TypeScript, JavaScript, data reporting, BigQuery, and other data tools.
- Experience designing and implementing efficient large-scale distributed systems through the full project life-cycle.
- Experience implementing data solutions for SAP ERP systems.
- Experience with Agile development methodologies and familiarity with web services and API ecosystems, or client-server environments.
- Experience setting up clean, well-structured, and accessible data feeds into machine learning models, enabling efficient training, validation, and deployment with the ability to identify and resolve complex data-related challenges.
Responsibilities
- Be passionate, friendly, motivated Data Architect with an interest in large-scale distributed systems, data and analytics infrastructure, data security, Artificial Intelligence and machine learning.
- Manage project goals, contribute to the organization's data strategy, and oversee the full life-cycle of AI/ML projects.
- Identify and implement improvements to data-related processes and workflows to enhance efficiency.
- Implement robust security measures to protect sensitive data and ensure adherence to privacy regulations. Ensure the data infrastructure can handle large volumes of data and high-performance computing requirements for AI/ML models.
- Drive continuous improvement in data architecture and stay updated on the latest data technologies and trends. Collaborate with infrastructure teams to build the necessary environment for deploying and monitoring ML models.
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