EQA’s strategic focus areas include scaling automation to support faster releases, adopting AI/ML solutions for test optimization, improving operational efficiency through workflow and tooling enhancements, aligning quality efforts across Engineering and Product, and supporting digital transformation through data‑driven quality practices.

The team’s strengths include deep domain expertise across core product areas, strong automation engineering capabilities, a broad understanding of end‑to‑end systems and integrations, a culture centered on collaboration and continuous improvement, and a forward‑looking approach to new technologies and methodologies.

Position Overview

The AI Software QA Engineer Intern will work with Fremont Bank’s Quality Assurance leadership to identify and implement Generative AI–based solutions that improve testing efficiency, speed, and quality across banking applications. This role focuses on applied AI experimentation, proof-of-concepts, and tooling to reduce manual effort in QA processes.

Role and Responsibilities Research and prototype Generative AI use cases to improve QA productivity Build proof-of-concept tools leveraging LLMs for Test case generation from requirements and user stories Test data generation and masking Defect analysis and clustering Regression test optimization and prioritization Analyze existing QA workflows and identify automation and AI opportunities Propose measurable efficiency improvements (time saved, coverage improved) Assist in integrating AI outputs into existing Selenium + C# automation framework Develop lightweight scripts, utilities, or dashboards to support QA teams Work with APIs, prompts, and AI services to evaluate feasibility and accuracy Document AI models, assumptions, limitations, and results Minimum Qualifications Currently pursuing a degree in Computer Science, Data Science, AI/ML, or related field Strong programming skills in at least one language (Python preferred; C# a plus) Understanding of basic software testing concepts Curiosity and willingness to experiment with Generative AI tools and APIs Experience with LLMs (OpenAI, Azure OpenAI, Hugging Face, etc.) Familiarity with test automation concepts (Selenium, Playwright, or similar) Exposure to prompt engineering or AI evaluation techniques Interest in fintech or regulated environments Progress Review

Weekly 1: 1 check‑ins with the mentor or Kavitha to discuss progress, roadblocks, findings, and next steps, while receiving coaching on technical skills, AI experimentation, automation practices, and prioritization. Bi‑weekly team demos or updates to present prototypes, findings, or automation improvements, receive input on design decisions and model quality, and build communication and demo skills. Track tasks, experiments, prototypes, and deliverables through Azure DevOps to provide transparency into progress, blockers, and workload, aligned with standard engineering practices. Mid‑point performance and progress review during weeks 5–6, assessing milestones, quality of deliverables, collaboration, communication, problem‑solving, initiative, and opportunities for improvement. Final project presentation in the last week, covering AI prototypes, automation or efficiency tools, workflow improvements, impact metrics, lessons learned, and future opportunities. Continuous feedback through Slack/Teams, code reviews, AI model reviews, and quick check‑ins to ensure real‑time guidance, unblock progress, and support experimentation with confidence. Experience and Knowledge Development

The intern will gain practical experience with AI and machine learning by building AI‑driven prototypes for QA use cases, working with generative AI for test case creation and code assistance, and learning prompt engineering, model evaluation, and experimental design. They will also develop real‑world software engineering skills through exposure to agile development workflows, CI/CD pipelines, version control, debugging, and writing automation scripts. In addition, the intern will build test automation and quality engineering capabilities by learning functional, regression, and integration testing, working with automation frameworks such as Selenium or Playwright, and understanding how test cases, test plans, and quality metrics support release cycles and DevOps processes. The role also offers hands‑on experience in data analysis and efficiency optimization, including evaluating QA workflow data, identifying inefficiencies, building small tools to improve operations, and engaging with dashboards and quality governance frameworks. Throughout the internship, the intern will strengthen professional and soft skills by collaborating with engineers and QA analysts, communicating technical findings to varied audiences, managing tasks and timelines, documenting prototypes, presenting results, and developing a creative, problem‑solving mindset.

What Makes Fremont Bank Associates Thrive? Team Players who go above and beyond to support their colleagues Action-Oriented professionals who challenge the status quo and seek improvement Purpose-Driven individuals who understand and champion the bank’s community impact Equal Opportunity Employer

Fremont Bank is proud to be an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees, regardless of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected classification.

Note to Search Firms

Fremont Bank does not accept unsolicited resumes from search firms or agencies without a signed service agreement. Unsolicited resumes will be considered the property of Fremont Bank, and no fees will be paid.