Lead Software Engineer - Databricks, 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 .

• 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 .