Lead Software Engineer - AI/ML Developer Lead
On-sitePlano, Texas, United States
Job Summary
Design, develop, and troubleshoot secure, high-quality production code while driving team adoption of enterprise-authorized AI-assisted engineering practices to improve delivery speed and operational outcomes. Lead evaluation sessions with external vendors and internal teams to probe architectural designs and applicability within existing systems, ensuring consistent validation standards for secure coding and automated testing. Apply technical troubleshooting to solve basic complexity issues and analyze large data sets to identify problems contributing to secure, stable application development. Requires 5+ years of applied software engineering experience, proficiency in Python and ML frameworks, and hands-on experience building and maintaining machine learning platforms. Full-time role within Consumer & Community Banking; sponsorship available.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure
- Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)
- Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure
- 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
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
Desired Qualifications
- Familiarity with Databricks for scalable data engineering and ML platform integration
- Experience working with Snowflake for cloud-based data warehousing and analytics
- Exposure to Snorkel AI for programmatic data labeling and training data management
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)
- Familiarity with feature stores, model registries, and ML metadata management
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)
- Experience with RESTful APIs and microservices architectures
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