Applied AI Engineer

at  ECPI UNIVERSITY LLC

OVERVIEW

The ideal candidate needs to be based in the United States.
The Applied AI Engineer plays a pivotal role in advancing ECPI University’s mission by crafting and deploying AI solutions to tackle real-world educational challenges. Working closely with our development and IT team of 50+ professionals, this position focuses on leveraging expertise in cloud-based AI infrastructures, machine learning, and software engineering to build intelligent systems that streamline university processes and elevate student experiences across different areas.

QUALIFICATIONS:

• Bachelor’s/Master’s degree in Computer Science, Data Science, or related fields, or equivalent experience in implementing AI solutions (degree is a plus, but not required).
• 2+ years of experience as an AI Engineer or similar role, with 5+ years in data science or software engineering.
• 1-2 years of experience in ML development and deployment using cloud-based ML infrastructure and MLOps.
• Proficiency in languages like Python, Java, or C++, and familiarity with AI libraries (e.g., TensorFlow, PyTorch).
• Strong problem-solving skills, adaptability, and expertise in document understanding techniques.
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and data analysis tools (e.g., Pandas, Matplotlib, Tableau).

• Solution Design: Collaborate with stakeholders to identify AI-driven opportunities, architect end-to-end AI systems, and oversee data handling, model selection, and deployment within the university’s framework.
• AI Algorithm Development: Create tailored AI algorithms and models, utilizing machine learning techniques to solve complex problems and enhance educational strategies.
• Model Training and Evaluation: Build, train, and optimize AI models while rigorously assessing performance.
• Model Deployment and Integration: Ensure seamless integration of AI solutions into production systems, focusing on scalability, reliability, and performance.
• Performance Optimization: Identify and rectify performance bottlenecks, optimizing models for efficiency.
• Data Analysis and Visualization: Perform exploratory data analysis, derive insights, and communicate findings through visualizations.
• Collaboration and Knowledge Sharing: Work closely with a large cross-functional team, sharing expertise and fostering innovation.
• Documentation and Reporting: Maintain comprehensive documentation and communicate project progress to stakeholders.