As a AI/ML Solutions Manager of Software Engineering at JPMorgan Chase in the Consumer and Community Banking Technology team, you will set strategic direction, oversee project delivery, and ensure alignment with business objectives for multiple engineering teams. Leveraging your leadership and technical expertise, you will guide the development of robust ML infrastructure and tools, foster a culture of technical excellence, and drive continuous improvement in platform capabilities. Your role will require exceptional collaboration and stakeholder management skills, as you empower teams, champion best practices, and represent the ML platform engineering function in cross-functional forums.

Job responsibilities

• Lead and manage engineering teams in the design, development, and maintenance of scalable machine learning platforms and infrastructure.
• Set strategic direction for ML platform initiatives, ensuring alignment with business goals and enterprise standards.
• Oversee the delivery of tools for model training, deployment, monitoring, and lifecycle management.
• Guide the integration of data engineering, feature management, and model serving capabilities into unified ML platform solutions.
• Ensure the implementation of secure, high-quality production code for platform services, APIs, and automation pipelines.
• Collaborate with data scientists, ML engineers, product teams, and business stakeholders to define requirements and deliver impactful platform features.
• Drive platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.
• Foster a culture of technical excellence, innovation, and continuous learning within the engineering team.
• Represent the ML platform engineering function in cross-functional forums and contribute to the community of practice.
• Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives.
   

Required qualifications, capabilities, and skills

• 5+ years of applied experience or formal training/certification in software engineering concepts, including coaching and mentoring.
• Proven experience building, deploying, and maintaining machine learning platforms or infrastructure.
• Proficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL).
• Strong understanding of cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure.
• Knowledge of MLOps practices, including CI/CD for ML, model versioning, and monitoring.
• Experience developing APIs and platform services for ML workflows.
• Solid understanding of the software development life cycle, agile methodologies, and engineering best practices.
• Demonstrated ability to lead and mentor engineering teams, and collaborate with cross-functional stakeholders.
• Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
   

Preferred qualifications, capabilities, and skills

• Experience with Databricks for scalable data engineering and ML platform integration.
• Experience with Snowflake for cloud-based data warehousing and analytics.
• Exposure to Snorkel AI for programmatic data labeling and training data management.
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow).
• Familiarity with feature stores, model registries, and ML metadata management.
• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Experience with RESTful APIs and microservices architectures.