Drivetrain is hiring Engineering Intern – Gen AI for FP&A Platform | Remote — Job Update | Vishal Kushwaha
Overview
Drivetrain is seeking highly motivated Computer Science engineering interns passionate about Generative AI to join their team. Interns will work on real-world projects involving Retrieval-Augmented Generation (RAG), Agentic AI, and Large Language Models (LLMs) to enhance the FP&A (Financial Planning & Analysis) platform. This is a remote internship opportunity.
Quick Snapshot
| Company | Drivetrain |
| Position | Engineering Intern – Gen AI for FP&A Platform |
| Location | India |
| Job Type | Internship |
| Work Mode | Remote |
Eligibility & Qualification
- Currently pursuing or recently completed a degree in Computer Science or a related field.
Required Skills
- Strong understanding of DSA (Data Structures & Algorithms), system design, and problem-solving.
- Familiarity with concepts such as RAG, Agentic AI, and LLMs. Completion of relevant projects is preferred.
- Demonstrated ability to build and showcase end-to-end projects in AI/ML or related fields.
- Excellent verbal and written communication skills.
Key Responsibilities
- Build and prototype Gen AI solutions using RAG, agentic workflows, and LLMs for FP&A use cases.
- Work closely with product and engineering teams to integrate AI-driven features into the platform.
- Apply strong computer science fundamentals to design efficient algorithms, data structures, and scalable systems.
- Clearly document your work, build workflow diagrams, and present results to the team.
- Complete and demonstrate end-to-end projects that highlight your technical and problem-solving skills.
Salary & Benefits
- Real-World Impact: Work on cutting-edge Gen AI projects for enterprise automation.
- Mentorship: Learn from industry experts and collaborate with a passionate team.
- Flexibility: Remote or hybrid options available to suit your schedule.
How to Apply
Interested candidates are encouraged to apply by sending their applications to careers@drivetrain.ai also through link.