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Listing record updated: October 9, 2026. We archive records after 60 days without an update. This date does not confirm that the employer is still hiring.

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About the position

The AI/ML Engineer Intern at Sertis will support the productionization of data science research and AI-driven solutions. Responsibilities include working alongside ML engineers to deploy AI models on diverse hardware platforms, building MLOps infrastructure, and writing production-grade software for model inference. The role involves contributing to generative AI projects and agentic systems while assisting in troubleshooting and technical documentation. Candidates should have a foundational understanding of machine learning concepts and the Software Development Life Cycle (SDLC).

Responsibilities

Support cross-functional teams in understanding business requirements and designing ML solutions

Assist in developing and maintaining scalable infrastructure for model training and deployment

Help develop cloud-native APIs, pipelines, and general software components for model inference

Gain exposure to cutting-edge foundation models and assist in building agentic systems

Assist in troubleshooting and resolving issues related to generative AI models and their implementations

Contribute to writing and maintaining clear technical documentation

Requirements

Currently studying a Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field

A foundational understanding of machine learning concepts and the Software Development Life Cycle (SDLC)

Good programming skills in Python, and ideally some familiarity with C++ or other languages used in ML applications

An awareness of Generative AI concepts and curiosity about real-world AI applications

Some exposure to LLM APIs such as OpenAI or Hugging Face through coursework, projects, or self-study

Good communication and interpersonal skills to effectively work with team members

Nice to have

Basic understanding of REST APIs and git version control; familiarity with CI/CD and MLOps principles

Exposure to cloud platforms (AWS, GCP, or Azure)

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