What are the responsibilities and job description for the Lead Software Engineer - Data bricks, ML, Cloud position at JPMorgan Chase?
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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 .