Skip to main content

Engineering Roles

At Hyletic, we value and prioritize the contributions of our engineering team. They play a crucial role in developing and maintaining our AI system platform. As an engineering-focused company, we offer various engineering roles that cater to different areas of expertise and responsibilities.

Software Engineer​

As a Software Engineer at Hyletic, you will be responsible for designing, developing, and maintaining software systems and applications. Your role will involve writing clean and efficient code, collaborating with cross-functional teams, and ensuring the scalability and reliability of our software solutions. Key responsibilities include:

  • Designing and implementing software solutions that align with business requirements and technical specifications.
  • Collaborating with product managers, designers, and other stakeholders to ensure the successful delivery of software projects.
  • Writing clean, maintainable, and efficient code following industry best practices and coding standards.
  • Conducting thorough testing and debugging to ensure the quality and reliability of software applications.
  • Troubleshooting and resolving software defects and performance issues.
  • Keeping up to date with the latest technologies, tools, and industry trends to continuously improve software development processes.

Data Engineer​

As a Data Engineer at Hyletic, your role will revolve around managing and optimizing our data infrastructure and pipelines. You will be responsible for designing and implementing data models, ensuring data quality and integrity, and developing efficient data processing and storage solutions. Key responsibilities include:

  • Designing and implementing data models and data architecture to support business requirements and analytical needs.
  • Building and maintaining data pipelines and ETL (Extract, Transform, Load) processes to ensure efficient data ingestion, processing, and storage.
  • Collaborating with data scientists and analysts to understand their data requirements and provide them with clean and reliable data.
  • Implementing data governance and data quality procedures to ensure the integrity and accuracy of our data assets.
  • Optimizing data storage and processing systems for performance and scalability.
  • Keeping up to date with the latest advancements in data engineering technologies and techniques.

DevOps Engineer​

As a DevOps Engineer at Hyletic, your role will involve bridging the gap between development and operations. You will be responsible for automating and streamlining our software development processes, ensuring the continuous integration and delivery of software solutions, and maintaining our infrastructure and deployment pipelines. Key responsibilities include:

  • Building and maintaining scalable and reliable infrastructure using cloud platforms like AWS, Azure, or GCP.
  • Designing and implementing CI/CD (Continuous Integration/Continuous Deployment) pipelines to automate software deployment and release processes.
  • Monitoring and optimizing system performance, availability, and reliability.
  • Implementing and maintaining security measures to protect our infrastructure and applications.
  • Collaborating with development teams to ensure smooth and efficient software development and deployment processes.
  • Troubleshooting and resolving infrastructure and deployment issues.
  • Keeping up to date with the latest DevOps tools, technologies, and best practices.

Machine Learning Engineer​

As a Machine Learning Engineer at Hyletic, your role will focus on developing and implementing machine learning models and algorithms to solve complex business problems. You will work closely with data scientists, software engineers, and domain experts to build AI-powered solutions. Key responsibilities include:

  • Designing and implementing machine learning models and algorithms to solve specific business problems.
  • Preprocessing and analyzing large datasets to extract meaningful insights and features.
  • Optimizing and fine-tuning machine learning models for performance and accuracy.
  • Collaborating with software engineers to integrate machine learning models into production systems.
  • Conducting experiments and evaluations to assess the performance and effectiveness of machine learning models.
  • Keeping up to date with the latest advancements in machine learning algorithms and techniques.