AI Engineer Intern, Database Performance Knowledge
Actian is seeking an AI Engineer Intern to join our 2026 Summer Internship Program, a 12-week remote initiative commencing on June 8th. This program is designed to provide interns with structured learning plans, dedicated mentorship, and a focused capstone project, culminating in an Internship Showcase. As a trusted leader in data management, integration, and analytics, Actian empowers businesses to unlock the full potential of their data across cloud, on-premises, and hybrid environments.
In this role, the intern will be responsible for developing a Results Augmented Generation (RAG) pipeline to gather system performance data and relevant administrative documents. This pipeline will enable a Large Language Model (LLM) to recommend configuration changes to the HCL Informix Database Server. Key tasks include configuring a chat client (VSCode) to interact with MCP Servers, creating MCP tools to capture server metrics, building a database with vectorized knowledge base documents using the HCL Vector blade for HCL Informix, and implementing the RAG workflow utilizing Actian's technologies.
Candidates must be actively enrolled in a college degree program and legally authorized to work in the United States. Required skills include proficiency in Python. Experience with RAG pipelines, VSCode, MCP concepts, SQL, and AI coursework are recommended.
The internship offers a competitive hourly wage ranging from $20 to $30. Participants will engage in regular intern events, workshops, and have opportunities to connect with Actian's executive team. Additionally, interns will be flown to Austin, Texas, for an onsite orientation, providing a blend of remote and in-person experiences.
Actian fosters a culture of innovation and collaboration, offering interns the chance to work on meaningful projects that address complex data challenges. This program provides valuable professional development, networking opportunities, and a platform to make a tangible impact within the organization.