AI Engineer, Entry Level

Emonics LLC; Phoenix (AZ); On-site; USD 70,000 - 90,000; Full time

Job summary

A technology solutions company is looking for an Entry Level AI Engineer to assist in building and deploying AI solutions. The position involves supporting AI model development for various applications, preparing datasets, and improving model performance. Candidates should have a degree in a relevant field and familiarity with machine learning libraries. The role requires collaboration with software and data engineers, offering a dynamic environment to grow skills in AI and ML applications.

Qualifications

• Bachelor’s or Master’s degree in a relevant field.
• Familiarity with supervised/unsupervised learning and evaluation metrics.
• Experience with common ML libraries like NumPy, pandas, and scikit-learn.

Responsibilities

• Support development of AI models for various tasks.
• Prepare datasets and perform feature engineering.
• Evaluate model performance to improve accuracy.
• Assist with deploying models into production.
• Collaborate with cross-functional teams.

Skills

Machine Learning Basics

Clear Communication

Curiosity

Education

Bachelor’s or Master’s degree in CS, Data Science, AI/ML, Statistics

Tools

NumPy

pandas

scikit-learn

Job description

We are hiring an Entry Level AI Engineer to help build and deploy practical AI solutions. You will work with structured and unstructured data, train and evaluate models, and help integrate AI into real applications.

What you will do

• Support development of AI models for classification, recommendation, forecasting, or NLP tasks
• Prepare datasets, perform feature engineering, and run experiments
• Evaluate model performance and help improve accuracy, latency, and robustness
• Assist with deploying models into production environments (APIs, batch jobs, pipelines)
• Collaborate with software engineers, data engineers, and product teams

What you are looking for

• Bachelor’s or Master’s degree in CS, Data Science, AI/ML, Statistics, or related field
• Familiarity with ML basics (supervised/unsupervised learning, evaluation metrics)
• Experience with common libraries (NumPy, pandas, scikit-learn)
• Clear communication and curiosity

Nice to have

• Exposure to deep learning (PyTorch or TensorFlow)
• Understanding of model deployment (FastAPI/Flask, Docker)
• Cloud fundamentals (AWS/GCP/Azure) and basic MLOps concepts