Job Description

As a Lead Software Engineer at JPMorgan Chase within the Corporate and Investment Banking Applied Artificial Intelligence and Machine Learning team, you will play a pivotal role in transforming the operations of the world's largest bank. You will collaborate with Data Scientists and Line of Business teams to integrate AI/ML solutions and develop horizontal capabilities, focusing on creating robust APIs, services, and libraries. This opportunity allows you to ensure seamless production integration and high-quality systems design, implementation, and delivery.

Job Responsibilities

Develop and maintain high-quality applications using Python, Kubernetes, Terraform and Kafka. Design and integrate AI/ML solutions into complex domain-specific document processing systems. Collaborate closely with other teams to understand and integrate with existing systems, proactively seek out and gather information necessary for systems integration and development. Architect scalable and resilient cloud infrastructure solutions using AWS and Kubernetes, ensuring performance and security for stream processing applications. Mentor and guide junior team members, lead initiatives to promote best practices and automation. Collaborate closely with SRE and production monitoring teams to ensure system reliability and performance.

Required Qualifications, Capabilities and Skills

Formal training or certification on Computer Science, Engineering, or related field, concepts and proficient advanced experience Proven hands-on experience with Python, Kubernetes, Terraform and AWS. Ability to work independently to understand and integrate with other systems within a bank. Proficiency in messaging and communication technologies such as Kafka and REST APIs. Ability to communicate technical information and ideas at all levels, convey information clearly and create trust with stakeholders. Strong understanding of containerization, microservices and streaming based architectures. Strong understanding of SDLC, continuous delivery and agile development practices.

Preferred Qualifications, Capabilities and Skills

Practical experience leading and mentoring small development teams. Practical experience deploying LLM based applications into production and an understanding of MLOPS. * Practice experience with data lakes, data catalogs and data retention best practice.