Description
Job Summary
pBecome a key player in transforming legacy mortgage servicing systems into cutting-edge technology. As an intern at BSI, you'll take the lead on building scalable solutions from concept to production using modern tools and technologies.
Responsibilities
- Engineer a scalable MVP from product requirements or PM-built POCs
- BUILD user-facing interfaces including web apps and dashboards with AI-assisted coding tools
- Develop data pipelines, ETL processes, and integrations on Databricks using dbt and PySpark
- Build and refine AI agents to automate complex workflows
- CLOSE loop with data engineers and analysts for ensuring data quality and pipeline reliability
- PARTICIPATE in code reviews, sprint planning, and agile ceremonies
Required Skills
- Python programming
- Data engineering using Databricks
- AI/ML with Python
- Web development (React or Streamlit)
- Coding tools proficiency (Claude Code, Cursor)
Required Skills Explained
- Scalable software engineering: Understanding of how to design and implement scalable systems.
- Data pipeline development: Proficiency in building, optimizing, and integrating data pipelines using tools like Databricks, PySpark, and dbt.
- AI/ML fundamentals: Basic understanding of machine learning concepts and their application in software engineering.
- Frontend & Internal Tools: Experience with React, Streamlit, or Retool for building user-facing interfaces and internal tools.
- Azure DevOps ecosystem: Familiarity with GitHub, Azure DevOps, Jira, and other CI/CD practices.
- Debugging and problem-solving: Ability to debug complex systems independently and quickly.
Who is this for
pThis role suits candidates who are eager to bridge the gap between theory and practice. Ideal for those pursuing a degree in Computer Science, Software Engineering, AI/ML, or related fields with hands-on experience.
Why This Job is a Good Opportunity
ulliHands-on experience: The role offers real-world experience in modern data stacks and AI-assisted coding tools, preparing you for a career as an engineer.liMentorship and growth: Opportunities to work closely with experienced engineers and contribute meaningfully from day one.liPotential full-time offer: Strong performers have the chance to transition into a full-time role post-internship.liImpactful projects: Work on legacy system modernization, contributing to significant industry changes in mortgage servicing.
Interview Preparation Tips
- Review AI and ML concepts: Be prepared to discuss your understanding of machine learning algorithms and their applications.
- Practice coding challenges: Familiarize yourself with data manipulation tasks using SQL and Python, as well as debugging techniques.
- Demonstrate hands-on experience: Showcase projects or contributions that align with the required tech stack (Databricks, PySpark, dbt).
- Discuss your workflow: Explain how you integrate AI coding tools like Claude Code, Cursor, and GitHub Copilot into your development process.
Career Growth in This Role
pThe role provides a solid foundation for a career in software engineering with a focus on data and AI. It offers the opportunity to work on cutting-edge technologies, contributing to industry transformations. You will gain valuable experience in modern software development practices, including cloud platforms and CI/CD pipelines.pAfter completing this internship, you can expect to have a strong portfolio of projects that demonstrate your skills in scalable systems, data engineering, and AI-assisted coding tools. This experience positions you well for roles in senior software engineer or similar technical leadership positions.
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Skills
Frequently Asked Questions
What are the qualifications needed for this role?Bachelor's or Master’s degree in Computer Science, Software Engineering, AI/ML, Data Science, or equivalent. Strong portfolio with meaningful commits and end-to-end projects.
Is there a possibility of full-time conversion after the internship?Strong performers may be considered for full-time roles upon successful completion of the internship.
What tech stack will I be working with?Databricks, PySpark, dbt, Azure Data Factory, SQL, Python, LLM APIs (Anthropic, OpenAI), React, Streamlit, GitHub, Azure DevOps, Jira.
