Associate AI Engineer

Assist in building and integrating AI-powered features into backend applications and workflows

Work with LLM APIs, machine learning models, and automation pipelines to support intelligent product experiences

Develop and maintain backend APIs, microservices, and integrations using modern development frameworks

Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions

Support the development of scalable data pipelines and workflows for AI-enabled applications

Participate in testing, debugging, monitoring, and improving production systems

Contribute to technical documentation, API specifications, and architecture discussions

Learn and apply best practices in software engineering, cloud development, and AI integration

Participate in code reviews and collaborate with senior engineers to improve code quality and system reliability

Stay current with emerging AI/ML technologies, tools, and engineering practices

Bachelor’s degree in Computer Science, Software Engineering, Data Science, AI, or a related field

Internship, co-op, academic project, or personal project experience in software engineering, AI/ML, or backend development

Proficiency in Python and familiarity with libraries or frameworks such as TensorFlow, PyTorch, or Scikit-learn

Understanding of backend development concepts such as APIs, microservices, and databases

Familiarity with JavaScript/TypeScript or Node.js is considered an asset

Exposure to REST APIs, SQL, and cloud platforms such as AWS or GCP through coursework, internships, or projects

Interest in AI/ML technologies, including LLMs and intelligent automation tools

Strong problem-solving skills and willingness to learn new technologies quickly

Good communication and collaboration skills in a team environment

Understanding of software development fundamentals, including testing, version control, and Agile development practices

Experience working on personal, academic, or hackathon projects involving AI or data platforms

Familiarity with tools such as Docker, Git, Kafka, or cloud deployment workflows

Exposure to data engineering, ETL pipelines, or analytics systems

Interest in scalable distributed systems or event-driven architectures

Contributions to open-source projects, technical clubs, or developer communities