Senior Specialty Software Engineer AI/ML Rule Engine
| Company | Wells Fargo |
|---|---|
| Location | West Des Moines, IA, USA, Chandler, AZ, USA, Irving, TX, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- 4+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 3+ years of experience with programming languages such as Java and C#, APIs, etc.
- 2+ years of design, develop, and deploy LLM-powered applications and services.
- 1+ year of experience with NLP techniques such as tokenization, embeddings, transformers, and attention mechanisms.
Responsibilities
- Lead or participate in complex initiatives on selected domains
- Assure quality, security and compliance for supported systems and applications
- Serve as a technical resource in finding software solutions
- Review and evaluate user needs and determine requirements
- Provide technical support, advice, and consultation with the issues relating to supported applications
- Create test data and conduct interfaces and unit tests
- Design, code, test, debug and document programs using Agile development practices
- Understand and participate to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
- Conduct research and resolve problems in relation to processes and recommend solutions and process improvements
- Assist other individuals in advanced software development
- Collaborate and consult with peers, colleagues and managers to resolve issues and achieve goals
Preferred Qualifications
- Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers)
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes)
- Experience with data preprocessing, feature engineering, and model evaluation
- Experience with deploying machine learning models in production environments and integrating ML into applications
- Knowledge of MLOps practices and tools
- Knowledge of Rule Engine Technology
- Experience with data preprocessing, feature engineering, and model evaluation.