Lead Software Engineer - Data bricks, ML, Cloud

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Corportate Sector, 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:

Provide technical leadership across design, development, and troubleshooting for complex, multi-domain solutions; establish engineering standards and best practices for the team . Write secure, high-quality code in Python and/or Java; conduct reviews and mentor engineers to raise code quality and maintainability . Build and productionize cloud-based ML pipelines; drive model deployment and operationalization in collaboration with Data Science and SRE/Platform teams . Own MLOps workflows; coordinate infrastructure and production changes with SRE; ensure resiliency, observability, and security across the ML lifecycle . Apply SDLC tooling and automation to improve delivery velocity and reliability; champion CI/CD and cloud-native best practices . Partner with Product Owners and business stakeholders to translate requirements into scalable solutions aligned to CCB Finance objectives . Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills: Formal training or certification on software engineering concepts and 5+ years applied experience. 8+ years of hands-on experience in software engineering, system design, application development, testing, and operational stability . Proficiency in Python and/or Java; strong grounding in secure coding practices . Cloud engineering experience building ML pipelines and deploying models to production with AWS services such as ECS, EMR, Lambda, EC2, SageMaker; familiarity with TensorFlow is a plus . Experience with PySpark, Kafka, Terraform, and Kubernetes for data processing, streaming, IaC, and container orchestration . Database experience with Oracle and/or Cassandra; familiarity with data modeling and query optimization preferred . Familiarity with CI/CD, application resiliency, security best practices, Agile/Scrum methodologies, and SDLC automation tools . Preferred qualifications, capabilities, and skills Background with machine learning frameworks, MLOps practices, and end-to-end ML lifecycle management (feature pipelines, model registry, monitoring, drift detection). Experience with the Python ML ecosystem (pandas, NumPy) and platforms such as Databricks for data engineering and model development at scale .