JPMorgan Lead Software Engineer 

India, Karnataka, Bengaluru 

391859821

Job responsibilities

• Collaborate with stakeholders across the organization to identify business needs and address challenges through comprehensive data analysis, visualization, and machine learning techniques.
• Leverage diverse and complex data sources, along with stakeholder-provided information, to generate decision support analytics and predictive models.
• Build and maintain Business Critical Reporting Solutions that deliver actionable insights, driving business efficiency and innovation through ML-driven analytics.
• Query, analyze, and transform complex data sets to optimize ETL processes and enhance performance, incorporating machine learning algorithms where applicable.
• Interface with business managers and technology teams to design, develop, deploy, and enhance reporting solutions, ensuring integration of ML models into production environments.
• Ensure all application development adheres to best practices, principles, and standards defined by the Banking Analytics team, while complying with the firm’s data control standards.
• Deliver high-quality solutions and establish processes for continuous improvement. Present data and analysis clearly and concisely to enable senior leadership to make informed, data-driven decisions.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Highly proficient with data warehousing and analytics tools, with hands-on experience in machine learning frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of cloud platforms such as AWS, with experience in Core Java and object-oriented design concepts and software design patterns.
• Proficient in programming languages such as Python and Java, with experience in developing and deploying machine learning models.
• Familiarity with distributed frameworks like Spark and Snowflake, and experience with AWS services including Lambda, EC2, EMR, Redshift, Glue, S3, IAM, RDS, Aurora, and DynamoDB.
• Infrastructure provisioning experience using CloudFormation, Terraform, or similar tools.
• Solid understanding of agile methodologies, including CI/CD, application resiliency, and security principles.
• Knowledge of Data Mesh Architecture principles and orchestration tools like Airflow

• Familiarity with data visualization tools such as QlikSense or Tableau.
• Experience in deploying machine learning models in production and monitoring their performance.