Onsite AI Engineer: ML Models & Production Deployment

DataJobs

Sunnyvale (CA)

On-site

USD 62,000 - 69,000

Full time

2 days ago
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Benefits offered by this job

Salary USD 45-50/hr
Dental insurance
Vision insurance

Job summary

BridgeNexus Technologies Inc is seeking an AI Engineer to build and deliver practical AI/ML solutions in Sunnyvale, CA on an onsite schedule. You will take models from data work through training, deployment, and monitoring in production, collaborating with engineers and data scientists.

The role focuses on large datasets, model training, and refining performance while maintaining production systems. A strong background in Python, ML frameworks, and cloud platforms is essential for success.

Qualifications

  • Strong programming skills in Python, R, or Java.
  • Knowledge of ML frameworks including TensorFlow and PyTorch.
  • Understanding of data structures and algorithms.
  • Experience with data analysis and statistics.
  • Familiarity with cloud platforms such as AWS, Azure, or GCP.
  • Bachelor's or master's degree in computer science, AI, or a related field.
  • Experience with AI/ML projects or internships.

Responsibilities

  • Develop and implement AI/ML models and algorithms.
  • Collect, clean, and analyze large datasets.
  • Train, test, and optimize machine learning models.
  • Deploy AI solutions into production environments.
  • Collaborate with software engineers and data scientists.
  • Monitor and improve model performance over time.

Skills

Python
R
Java
Data structures
Algorithms
Data analysis
Statistics
AI/ML projects

Education

Bachelor's or Master's degree in CS/AI or related field

Tools

TensorFlow
PyTorch
AWS
Azure
GCP
Hadoop
Spark

Job description

BridgeNexus Technologies Inc is seeking an AI Engineer to build and deliver practical AI/ML solutions in Sunnyvale, CA on an onsite schedule. You will take models from data work through training, deployment, and monitoring in production, collaborating with engineers and data scientists.

The role focuses on large datasets, model training, and refining performance while maintaining production systems. A strong background in Python, ML frameworks, and cloud platforms is essential for success.

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