Job Description & Details
This is an entry-level AI research and prototyping role tailored for recent grads or students looking to break into the Generative AI space. Rather than traditional QA testing, you'll be heavily involved in evaluating LLMs, prompt engineering, and building quick proof-of-concept solutions alongside senior leadership. If you are deeply curious about how large language models work under the hood and want hands-on experimentation time, this is a solid entry point.
What You'll Actually Be Doing
Expect your day-to-day to be a mix of technical experimentation and research. You'll spend hours designing and stress-testing prompts for various business use cases, digging into vector databases or RAG pipelines, and figuring out how to integrate LLM outputs into actual internal workflows. You won't be writing production-grade microservices from scratch, but you will be scripting in Python, hacking together quick prototypes, and documenting how different AI platforms perform across sectors like healthcare and finance.
The Core Tech Stack
You need a foundational understanding of Python and a strong conceptual grasp of Large Language Models like ChatGPT, Claude, and Gemini. Knowing your way around Jupyter Notebooks, Git, and basic API integrations is going to keep you afloat. If you've tinkered with LangChain, LlamaIndex, or Hugging Face in your personal projects or coursework, make sure to highlight that front and center, as it bridges the exact gap this team is looking to close.
Interview Expectations
Expect the interviewers to probe your understanding of prompt engineering mechanics and failure modes in LLMs, such as how you would mitigate hallucinations in a retrieval-augmented generation pipeline. They want to see how you troubleshoot ambiguous technical problems, so be ready to walk them through a personal or academic AI project where something broke and how you debugged it.
Application Advice
Skip the corporate fluff on your resume and heavily feature your academic or personal projects involving Python, APIs, and generative AI tools. Mention specific frameworks like LangChain or Hugging Face if you've used them. ATS algorithms will scan for keywords like 'Prompt Engineering', 'Python', 'LLMs', and 'Generative AI', so weave those terms naturally into your project descriptions rather than hiding them in a generic skills list.