Develop, test and maintain scalable Python applications. Write reusable code, integrate frontend and backend systems, collaborate with developers and data teams, troubleshoot issues, participate in code reviews, and contribute to product development. Support deployment and performance optimization while following coding best practices.

Some of the key job functions Provide support of incoming tickets, including extensive troubleshooting tasks, with responsibilities covering multiple products, features and services Work on operations and maintenance driven coding projects, primarily in Ruby, Rails, Java, Python, or shell scripts,AWS, web technologies projects. Software deployment support in staging and production environments Develop tools to aid operations and maintenance The Amazon Consumer Payments supports multiple payment.

Key Responsibilities: Extract large data from various data sources using multiple tools such as Python/Pyspark/Databricks/ SQL etc., to perform multiple analysis Manage all analytical solutions that includes Design, Development, Validation, Calibration, Documentation, Implementation, Monitoring, and Reporting Learn to generate Analytical Insights on various portfolios Utilize latest data science techniques across both supervised and unsupervised machine learning methodologies, NLP, and developm.

Job Summary We are looking for a skilled Data Engineer with 4 years of experience in Python, SQL, and Power BI. The candidate will be responsible for building and maintaining data pipelines, transforming and analyzing data, and developing dashboards to support business decision-making. Key Responsibilities Design, develop, and maintain scalable ETL/ELT data pipelines. Extract, transform, and load data from multiple sources using Python and SQL. Develop and optimize complex SQL queries, stored p.

Strong Data Scientist / AI Engineer / Generative AI Engineer profile. Mandatory (Experience 1) - Must have 3 years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development. Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support. Mandatory (Experience 3) - Must have experience working with Machine Lea.

Develop recommendation systems and machine learning solutions in production environments. Design, deploy, monitor, and optimize ML models, support ETL pipelines, data warehousing, feature engineering, and cloud-based analytics. Collaborate with engineering teams to build scalable AI-driven systems and enhance recommendation accuracy and business outcomes.

Design, develop, and deploy machine learning and deep learning models. Analyze large and complex datasets to identify patterns and trends, perform statistical analysis, and implement predictive analytics solutions that support business decision-making and operational improvements across enterprise functions.

Design and develop software solutions, build statistical and machine learning models, perform data engineering activities, create data pipelines, conduct data analysis, and support dynamic pricing and revenue management use cases. Train and calibrate ML models, define KPIs, conduct research on new algorithms, and support deployment, testing, and production investigations.

Develop, test, and maintain Python-based applications while working with PostgreSQL and SQL databases. Design backend solutions, optimize queries, debug existing systems, collaborate with development teams, follow coding standards, and contribute to scalable software delivery. Support data-driven applications and enterprise development initiatives.

Design, develop, and maintain scalable Python-based backend applications and services. Collaborate with cross-functional teams to define and deliver new features, write efficient and maintainable code, troubleshoot and upgrade existing systems, participate in code reviews, and contribute to software engineering best practices. Support enterprise-grade applications and cloud-based deployments.