AI ML Engineer
Tekdoors Inc
Phoenix, United States of America
31 days ago
Role details
Contract type
Temporary contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
EnglishJob location
Phoenix, United States of America
Tech stack
Java
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Big Data
C++
Computer Programming
Continuous Integration
Monitoring of Systems
Python
Machine Learning
NoSQL
NumPy
Performance Tuning
TensorFlow
Software Construction
SQL Databases
Data Processing
PyTorch
Deep Learning
Pandas
Containerization
Scikit Learn
Kubernetes
Information Technology
Machine Learning Operations
Artificial Intelligence Markup Language (AIML)
Docker
Job description
- Design, build, and optimize machine learning models for real-world applications
- Develop and deploy scalable AI solutions in production environments
- Work with large datasets to preprocess, clean, and transform data
- Collaborate with data scientists, software engineers, and product teams
- Implement model monitoring, validation, and performance tuning
- Build and maintain data pipelines and ML workflows
- Stay updated with the latest advancements in AI and machine learning
- Document processes, models, and systems for reproducibility
Requirements
We are looking for a skilled AI/ML Engineer to design, develop, and deploy machine learning models and AI-driven solutions. The ideal candidate will have strong experience in data science, model development, and scalable deployment, along with a solid understanding of algorithms and software engineering best practices., * Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field
- Strong programming skills in Python (preferred) or Java/C++
- Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn
- Solid understanding of machine learning algorithms (supervised, unsupervised, deep learning)
- Experience with data handling libraries such as Pandas and NumPy
- Familiarity with SQL and NoSQL databases
- Knowledge of cloud platforms (AWS, Azure, or GCP)
- Understanding of MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes).