Senior AI Platform Lead Engineer

JPMorgan Chase & Co.

Jersey City (NJ)

On-site

USD 180,000 - 240,000

Full time

8 days ago

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Job summary

JPMorgan Chase & Co. seeks a Senior Lead Software Engineer to join the Consumer & Community Banking AI/ML Platform Engineering Team in Jersey City.

You will design, build, and scale robust ML platforms, enabling data scientists and ML engineers to develop, deploy, and monitor models securely. You will lead a team in delivering platform services, APIs, and automation pipelines while driving adoption of enterprise AI-assisted engineering practices and maintaining alignment with enterprise

Qualifications

Formal training or certification in software engineering concepts.
Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure.
Strong Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
Experience with data processing frameworks and tools like Spark, Pandas, SQL.
Practical experience with cloud-based ML platforms (AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure.
Solid understanding of MLOps: CI/CD for ML, model versioning, monitoring.
Experience developing APIs and platform services for ML workflows.
Proficient in SDLC and Agile methodologies.
Ability to collaborate with cross-functional teams to deliver platform solutions.
Experience adopting enterprise AI-assisted engineering practices for code quality and delivery speed.

Responsibilities

Designs, builds, and maintains scalable ML platforms and infrastructure to support end-to-end ML workflows.
Develops and optimizes tools for model training, deployment, monitoring, and lifecycle management.
Integrates data engineering, feature management, and model serving into unified ML platform solutions.
Implements secure, high-quality production code for platform services, APIs, and automation pipelines.
Leads evaluation sessions with data scientists, ML engineers, and product teams to understand requirements and deliver platform features.
Ensures platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.
Produces architecture and design artifacts for platform components, ensuring enterprise standards and best practices.
Automates infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.
Contributes to the ML platform engineering community of practice and participates in events exploring new technologies.
Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, speed, and outcomes, with standardized validation.
Applies SDLC toolchain knowledge to improve automation value.