AI Engineer

SUMMARY:
Role:
An AI automation engineer’s responsibility may include some or all of the following: Building AI-powered
tools using prompt engineering and automated evaluations: Prompt engineering involves fine-tuning what
you input into an AI system to get the best results for your specific use case.
 
An AI engineer with automation specializes in integrating AI models (like GPT or computer vision) into existing business processes to automate complex, repetitive tasks. They build, test, and deploy automated workflows using tools like Python, APIs, and RPA platforms to enhance efficiency, reduce human error, and streamline data workflows.
 
Workflow Automation: Design and implement AI-driven automated workflows (e.g., automated
document processing, intelligent chatbots, customer service automation).
System Integration: Connect disparate platforms (e.g., Slack, Stripe, HubSpot, CRMs) using APIs and
webhooks.
AI Model Development: Develop, test, and deploy machine learning algorithms (using TensorFlow,
PyTorch) and RPA bots (UiPath, Automation Anywhere).
Monitoring & Optimization: Monitor system performance, ensure scalability, and refine models based
On data feedback to increase efficiency.
Architecture & Security: Design scalable, secure systems that comply with data privacy standards.
 
Required skills and qualifications:

• Programming: Proficiency in Python, Java, or .NET.
• AI/ML Frameworks: Scikit-learn, TensorFlow, PyTorch, or similar.
• Automation/RPA Tools: UiPath, Blue Prism, or Microsoft Power Automate.
• Cloud & DevOps: Experience with AWS, Azure, or GCP, plus MLOps practices.
• Database & API: Proficiency in SQL, APIs, and RESTful services.
• Education: Bachelor’s or Master’s degree in Computer Science, AI, or related fields.

POSITION INFO:
Role:
An AI automation engineer’s responsibility may include some or all of the following: Building AI-powered
tools using prompt engineering and automated evaluations: Prompt engineering involves fine-tuning what
you input into an AI system to get the best results for your specific use case.

An AI engineer with automation specializes in integrating AI models (like GPT or computer vision) into existing business processes to automate complex, repetitive tasks. They build, test, and deploy automated workflows using tools like Python, APIs, and RPA platforms to enhance efficiency, reduce human error, and streamline data workflows.

Workflow Automation: Design and implement AI-driven automated workflows (e.g., automated
document processing, intelligent chatbots, customer service automation).
System Integration: Connect disparate platforms (e.g., Slack, Stripe, HubSpot, CRMs) using APIs and
webhooks.
AI Model Development: Develop, test, and deploy machine learning algorithms (using TensorFlow,
PyTorch) and RPA bots (UiPath, Automation Anywhere).
Monitoring & Optimization: Monitor system performance, ensure scalability, and refine models based
On data feedback to increase efficiency.
Architecture & Security: Design scalable, secure systems that comply with data privacy standards.

Required skills and qualifications:

• Programming: Proficiency in Python, Java, or .NET.
• AI/ML Frameworks: Scikit-learn, TensorFlow, PyTorch, or similar.
• Automation/RPA Tools: UiPath, Blue Prism, or Microsoft Power Automate.
• Cloud & DevOps: Experience with AWS, Azure, or GCP, plus MLOps practices.
• Database & API: Proficiency in SQL, APIs, and RESTful services.
• Education: Bachelor’s or Master’s degree in Computer Science, AI, or related fields.