Lead Software Engineer - Machine Learning

Join our team as a Full Stack Software Engineer and play a pivotal role in shaping the future of Machine Learning and AI solutions within the Consumer & Community Banking sector at JPMorgan Chase.

As a Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking, you are 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Develops secure high-quality production code, and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
• Adds to team culture of diversity, equity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Good experience of various machine learning algorithms, including supervised and unsupervised learning, neural networks and reinforcement learning
• Solid experience with Python, SQL, CI/CD pipeline, Github, AWS or Azure or GCP required
• End-to-end working knowledge on how a machine learning product is built
• Familiarity with MLOPS and components in MLOPS ecosystem
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s)
• Proficiency in automation and continuous delivery methods
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Practical cloud native experience

Preferred qualifications, capabilities, and skills

• In-depth knowledge of the financial services industry and their IT systems
• Practical AWS or Azure cloud native experiences
• Experience with deploying applications using Docker, Kubernetes
• Experience in data streaming tools such as Kafka
• Experience with Generative AI and LLM’s