Qode world

This is a position we're hiring on behalf of our client. They are a highly skilled & multicultural team with expertise in Cloud Computing Solutions, DevOps, and Managed Services as well as proven capabilities in the execution of advanced technologies projects.

As an AI Data Engineer, you will play a crucial role in designing and building scalable data pipelines, infrastructure, and machine learning systems to support AI-driven projects. You will collaborate closely with AI Product Managers, Data Scientists, and Software Engineers to develop AI models and solutions tailored to business needs.

Your key responsibilities will include:

• Data Pipeline Development: Designing, building, and maintaining scalable data transformation, ingestion, and storage pipelines to support AI and machine learning applications
• AI Model Deployment: Collaborating with data scientists and AI engineers to deploy machine learning and AI models into production environments
• Infrastructure Automation: Automating AI infrastructure to streamline development workflows for data scientists and AI engineers
• Model Development & Integration: Assisting in building AI models from the ground up, transforming machine learning models into APIs, and integrating them with other systems for real-world applications
• Statistical Analysis: Conducting statistical analysis to interpret model results and inform business decisions
• AI Experimentation: Running experiments and tests for machine learning models and AI applications, ensuring optimal performance
• Collaboration: Partnering with cross-functional teams, including electronics and robotics departments, to integrate AI solutions effectively
• System Maintenance: Monitoring and retraining systems to ensure continuous improvement and alignment with evolving business goals
• Stakeholder Communication: Translating complex AI outcomes into actionable insights for business users and stakeholders
• Other tasks required by CTO/ CEO
  
  
  

Requirements

Must-haves:

At least 3 years of experience in deploying AI models

• Educational Background:
  
  

○ Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial

Intelligence, or a related field.

• Technical Expertise:
  
  

○ Extensive experience in building data pipelines and deploying AI/ML models into

production environments.

○ Proficiency in programming languages such as Python, Java, C++, or R.

○ Strong knowledge of statistics, calculus, and probability for algorithm development

and model evaluation.

○ Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or

Scikit-learn.

• Cloud Experience:
  
  

○ Expertise in cloud services (e.g., AWS, Azure, or Google Cloud) with at least 3 years

of experience in deploying AI models.

○ Must hold a relevant cloud certification, such as AWS Certified Data Analytics,

AWS Certified Machine Learning Specialty, or equivalent.

• AI Specializations:
  
  

○ Familiarity with natural language processing, computer vision, and common AI model

architectures.

○ Experience in transforming AI models into APIs for integration with other sytems.

• Additional Skills:
  
  

○ Solid understanding of data engineering practices, including ETL, feature

engineering, and data preprocessing.

○ Ability to work collaboratively in an agile and fast-paced environment.

○ Strong problem-solving and communication skills.

○ Demonstrated ability to run AI experiments and optimize performance through

testing.

• Preferred Qualifications:
  
  

○ Experience with robotics or electronics integration.

○ A track record of contributing to end-to-end AI product development.

Benefits

Competitive, affordable, and diverse benefit programs that support your total health - from a healthy body to a healthy mind.

• Work with the latest cloud technologies and participate in diverse international projects;
• A dynamic work environment with helpful colleagues, working with people of many nationalities;
• Access to a complete set of learning platforms and continuous learning opportunities (certificates, boot camps, languages, etc.);
• Private medical insurance;
• Lunch voucher;
• Parking account;
• Well-being programs participation;
• Participate in the company's training programs, AWS certification
• Participate in company events and company trips;