Lead Software Engineering - Python / Kubernetes / AWS

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.