Job Description & Details
This is a hybrid AI/ML engineering role supporting public infrastructure systems where you'll be designing, developing, and deploying models that actually impact daily operations. While the day-to-day is mostly virtual, you do need to be within a three-hour commuting distance of Middletown, PA, for occasional onsite deployments, emergency responses, and the initial hardware pickup.
What You'll Actually Be Doing
Expect to spend your time building and fine-tuning predictive models, setting up data pipelines, and troubleshooting existing AI applications that might be acting up in production. You will bridge the gap between raw data and actionable insights, which means translating complex analytical findings into presentations for non-technical stakeholders. Collaboration is key here since you'll be working with cross-functional teams to tackle infrastructure challenges using computer vision and natural language processing.
The Core Tech Stack
Python is non-negotiable for this gig, and you'll need a solid grasp of standard machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Because this environment heavily integrates with Microsoft's ecosystem, experience with Azure, Azure Synapse, and managing data workflows is critical for moving models smoothly from local experimentation to production pipelines.
Interview Expectations
Expect the hiring team to test your system design capabilities around deploying ML models in cloud environments like Azure, focusing heavily on how you handle latency and pipeline bottlenecks. They will likely present a scenario where a deployed computer vision or NLP model degrades in production, and they want to hear your methodical approach to diagnosing the drift, retraining, and safely pushing out a fix.
Application Advice
Your resume needs to heavily feature concrete keywords from this listing, specifically highlighting your hands-on experience with Azure Synapse, NLP, and computer vision projects. Don't just list frameworks—write brief bullet points detailing how you optimized a machine learning pipeline or improved an existing model's performance to get past the automated screeners and grab the technical manager's attention.