Senior AI / ML Engineer - (Only W2)

Craftner INCabout 1 year ago
Phoenix, Arizona, United States
On-site
Contract
Junior Level (1-3 years)

Job Description

Position Overview

We are looking for an expert AI / ML Engineer (Only W2) with 16+ years of experience to join our team in Phoenix onsite. In this long term contract role, you will lead all processes from data collection, cleaning, and preprocessing to training models and deploying them to production. The ideal candidate will be passionate about artificial intelligence and stay current with the latest developments in the field.

Key Responsibilities

  • Understand business objectives and develop models with metrics to track progress
  • Manage resources such as hardware, data, and personnel to meet deadlines
  • Analyze and rank AI/ML algorithms for problem-solving
  • Explore and visualize data to identify differences that may affect model performance
  • Verify data quality through cleaning or process supervision
  • Supervise data acquisition and source additional datasets as needed
  • Define validation strategies and preprocessing/feature engineering pipelines
  • Implement data augmentation pipelines, train models, and tune hyperparameters
  • Analyze model errors and develop strategies to overcome issues
  • Deploy models to production

Required Qualifications

  • Proficiency with deep learning frameworks such as TensorFlow or Keras
  • Strong skills in Python and libraries like scikit-learn and pandas
  • Expertise in visualizing and manipulating big datasets
  • Experience with OpenCV and familiarity with Linux
  • Ability to select appropriate hardware for ML models ensuring required latency

Benefits & Perks

  • Compensation: $44.65 - $53.78 per hour
  • Job Type: Contract
  • Schedule: 8 hour shift, Monday to Friday
  • Work Location: Onsite in Phoenix
  • Preferred Employment: W2 / 1099

Required Skills

Hyperparameter Tuning
scikit-learn
Python
Linux
Data Visualization
OpenCV
TensorFlow
Data Cleaning
Data Collection
pandas
Keras
Model Training
Model Deployment