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Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer - Java, AWS at JPMorgan Chase within the Commercial & Investment Bank - Securitized Products Group, you'll be an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
You will mentor senior engineers, foster a culture of ownership and innovation, and partner closely with business and executive stakeholders to align technology investments with organizational priorities.
Influences peers and project decision-makers to consider the use and application of leading-edge technologies
Required qualifications, capabilities, and skills
Practical cloud native experience
JPMorgan Chase Bank, N.A., doing business as Chase Bank or often as Chase, is an American national bank headquartered in New York City, that constitutes the consumer and commercial banking subsidiary of the U.S. multinational banking and financial services holding company, JPMorgan Chase
Job ID: 148408625
Skills:
Java, Databases, Machine Learning, Spring Boot, System Design, Python, AWS, Microservices, Cloud Native, Event-Driven Architectures, Ai
Skills:
Pyspark, Spark SQL, Terraform, Python, Sql, Etl, Apache Spark Framework
Skills:
AI ML, Machine Learning, Artificial Intelligence, LLMs, custom silicon, Trainium, Amazon Q, AI infrastructure, GPUs, RAG model, Amazon Bedrock, SageMaker, accelerated compute, ML Ops, Generative AI, Inferentia
Skills:
Git, Docker, Terraform, PostgreSQL, Jira, Kubernetes, Linux Administration, Python, Airflow, FinOps best practices
Skills:
Xml Parsing, Sql, Fme, Aws Services, Python, Cloud Architecture, Qgis, Tableau, Arcgis, geospatial machine learning, automated QA pipelines, automated geospatial quality-testing methodologies
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