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Software Engineer III - ML Pipelines(Python, Pyspark , AWS)

JPMorgan Chase
Plano, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 3 days ago
awsdatabricksdockerecskubernetesnumpy

Full Job Posting

Job Responsibilities:

  • Design, build, and maintain platform components that support end-to-end ML model lifecycle — from development and training to deployment and monitoring
  • Develop and maintain data and feature pipelines that feed ML models in production
  • Build and manage cloud-based infrastructure on AWS (including Databricks, EMR, ECS, and S3) to support model training and serving workloads
  • Automate model deployment, testing, and release processes within the SDLC/MLOps toolchain
  • Support migration of legacy ML workloads to cloud-native, scalable platforms with zero downtime
  • Monitor platform health and model serving infrastructure; identify and resolve performance and stability issues
  • Apply AI-assisted development tools and best practices to improve code quality and delivery speed
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • 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.
  • Collaborate with data scientists and model developers to understand requirements and translate them into reliable platform capabilities
  • Contribute to a team culture of diversity, inclusion, and continuous improvement

Required Qualifications, Capabilities, and Skills:

  • Formal training or certification in software engineering and 3+ years of applied experience
  • Hands-on experience building and maintaining production data or ML pipelines
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
  • Proficiency in Python and experience with ML libraries and frameworks (Pandas, NumPy, Scikit-learn, etc.)
  • Working knowledge of cloud platforms, particularly AWS, and cloud-native development patterns
  • Experience with Big Data processing frameworks (Spark preferred)
  • Practical experience with infrastructure-as-code and deployment automation (Terraform preferred)
  • Ability to work independently on platform problems with moderate oversight

Preferred Qualifications, Capabilities, and Skills:

  • Experience with Databricks for model training and data pipeline development
  • Familiarity with MLOps practices — model versioning, experiment tracking, feature stores, and model monitoring
  • AWS certifications (e.g., Solutions Architect Associate)
  • Exposure to RAG architectures or GenAI/LLM integration patterns
  • Knowledge of container-based deployment (Docker, ECS, or Kubernetes)
  • Interest in AI-assisted engineering tools and automation within the SDLC
  • Experience with Big Data processing frameworks (Spark preferred)
  • Practical experience with infrastructure-as-code and deployment automation (Terraform preferred)

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