Job Description & Details
This is a hands-on software engineering role focused on building internal automation tools and integrating AI agents into team workflows. You'll spend your time bridging the gap between legacy systems and modern automation, working out of the Warren office three days a week.
What You'll Actually Be Doing
Expect to spend a good chunk of your week observing how different engineering teams operate to identify friction points and manual bottlenecks. Once you spot a tedious process, you'll design, code, and deploy internal tools—frequently leveraging AI to make things run smoother. You won't just write code and throw it over the wall; you'll own the full lifecycle, from requirements gathering and unit testing to creating migration packages and writing documentation so your team can actually use what you build.
The Core Tech Stack
You need to be very comfortable with Python, as it's the primary engine behind these automation scripts and AI tools. Beyond basic scripting, they expect a strong computer science foundation—specifically data structures, algorithms, and software design principles—because you'll be dealing with large systems software and troubleshooting both new code and legacy production systems. Experience building actual AI agents and familiarity with platforms like Databricks or Claude will instantly put you at the top of the pile.
Interview Expectations
Expect them to probe deeply into how you handle system design and debugging under pressure; they will likely ask you to walk through a time you had to troubleshoot a failing legacy production system without breaking downstream dependencies. The hiring manager is secretly looking for systematic root-cause analysis rather than just quick hacks. You'll also probably be quizzed on your practical experience with AI tools and agents, specifically how you handle hallucination, error-handling, and state management when building autonomous workflows.
Application Advice
Make sure your resume doesn't just list languages, but explicitly highlights projects where you built internal tools or automation scripts from scratch. Weave keywords like 'Python', 'AI agents', 'unit testing', and 'large systems design' naturally into your bullet points to easily clear the automated ATS filters. If you have any specific experience integrating large language models or working with data platforms like Databricks, bring those to the absolute forefront of your experience section.