Highlights
Develop LLM pipelines including prompt engineering, prompt chaining, memory, tool calling, and multi-agent coordination. " Deployed models using REST APIs, Docker, or cloud platforms (AWS / Azure / GCP) to support production.
Numbers & Facts
| Location | CHANDLER, AZ |
| Industry | Computer/IT Services |
| Company Size | 500 to 999 employees |
| Year Founded | 1990 |
| Website | http://q1tech.com/ |
Description
Job Title: Python Unix machine learning Support Engineer
Duration- Fulltime Permanent
Location: Chandler, AZ (Onsite from Day1)
Job Description:
Python Unix machine learning Support Engineer
Must Have Technical/Functional Skills
Unix, Shell, Scripting, Python, Machine learning, Production Support
Roles & Responsibilities
" System Configuration: Configuring Unix systems to meet specific requirements and standards.
" Troubleshooting: Identifying and resolving issues with Unix systems and applications.
" Scripting: Automating repetitive tasks using Python scripts.
" Performance Optimization: Analyzing and improving the performance of Unix systems.
" Documentation: Creating and maintaining system documentation and guides.
" Collaboration: Working with other teams and departments to ensure Unix systems are integrated and functional.
" Implement AI workflows using Python, agent frameworks, and orchestration tools
" Develop LLM pipelines including prompt engineering, prompt chaining, memory, tool calling, and multi-agent coordination
" Integrate LLMs with enterprise systems and APIs
" These roles are essential for maintaining the reliability and efficiency of Unix-based systems, and Python skills
can be leveraged to automate and streamline these tasks.
" Designed, developed, and deployed machine learning models using supervised and unsupervised learning
techniques to solve real world business problems.
" Worked with Python ML libraries including Scikit learn, TensorFlow, PyTorch, Pandas, NumPy, and Matplotlib.
" Deployed models using REST APIs, Docker, or cloud platforms (AWS / Azure / GCP) to support production
use cases.
Duration- Fulltime Permanent
Location: Chandler, AZ (Onsite from Day1)
Job Description:
Python Unix machine learning Support Engineer
Must Have Technical/Functional Skills
Unix, Shell, Scripting, Python, Machine learning, Production Support
Roles & Responsibilities
" System Configuration: Configuring Unix systems to meet specific requirements and standards.
" Troubleshooting: Identifying and resolving issues with Unix systems and applications.
" Scripting: Automating repetitive tasks using Python scripts.
" Performance Optimization: Analyzing and improving the performance of Unix systems.
" Documentation: Creating and maintaining system documentation and guides.
" Collaboration: Working with other teams and departments to ensure Unix systems are integrated and functional.
" Implement AI workflows using Python, agent frameworks, and orchestration tools
" Develop LLM pipelines including prompt engineering, prompt chaining, memory, tool calling, and multi-agent coordination
" Integrate LLMs with enterprise systems and APIs
" These roles are essential for maintaining the reliability and efficiency of Unix-based systems, and Python skills
can be leveraged to automate and streamline these tasks.
" Designed, developed, and deployed machine learning models using supervised and unsupervised learning
techniques to solve real world business problems.
" Worked with Python ML libraries including Scikit learn, TensorFlow, PyTorch, Pandas, NumPy, and Matplotlib.
" Deployed models using REST APIs, Docker, or cloud platforms (AWS / Azure / GCP) to support production
use cases.
About Company
Q1 consists of experienced and recognized experts providing the capability to respond to market demand in order to provide professional services for our clients including Enterprise software implementations, application integration and technical / functional support.
Q1 has steadily grown into a Quality IT services and solutions organization with the average experience of our team being over 10 years. We have continuously met or exceeded client expectations by delivering professional services and project implementations on time and under budget to help clients truly recognize return on investment.
