Technical lead building safe, evaluable LLM agents and AI infrastructure for OpenLoop’s telehealth platform. Setting architecture, observability, retrieval, and model operations direction.
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Applied AI Engineer – Agentic Workflows, RAG – Master-Level Internship
Posted 3 months ago
About the role
- Applied AI Engineer Intern designing intelligent workflows and retrieval systems for innovative AI solutions at Vosyn. Seeking Master’s level candidates with strong AI coding experience and creativity.
Responsibilities
- Design, build, and harden agentic workflows that plan and take actions reliably — and understand why agents fail (context loss, compounding errors, no feedback signal) and how to structure tasks so they succeed.
- Build retrieval (RAG/search) pipelines that fetch the right client data and ground model outputs in it, integrated into core applications rather than demos.
- Select the right model for each task — reasoning model vs. fast instruction model — and be able to justify the trade-off in latency, cost, and quality.
- Engineer prompts and context structures appropriate to the model class, including knowing when reasoning models need framing rather than step-by-step hand-holding.
- Write evaluations for AI features, because with non-deterministic models “it worked once” is not evidence that it works.
- Connect AI tools to internal systems and data sources via APIs or MCP to power real client use cases.
- Review and validate AI-generated code and automated workflows critically for correctness, security, and safety.
- Collaborate with the Builder and the Integration & Data Engineer to deliver complete, working solutions, and document workflows, prompts, and integrations in Notion.
Requirements
- Currently enrolled or recently graduated from a Master’s program in Computer Science, Software Engineering, AI/ML, Information Systems, or a related field. Master’s program enrollment or completion is mandatory.
- Strong, demonstrable hands-on experience with AI coding assistants and the Claude API or comparable model APIs — portfolio, GitHub, or live examples strongly preferred.
- A working understanding of how modern LLMs and reasoning models behave: context windows, the difference between reasoning and instruction models, and when to reach for each.
- Practical experience with at least one of: building an agentic workflow, building a RAG/retrieval pipeline, or integrating models via tool calling or MCP.
- Excellent prompt-engineering and context-management skills.
- Coding fluency in JavaScript/React and/or Python sufficient to build, evaluate, and fix AI-generated output.
- An instinct for evaluation: you want to measure whether the AI is actually correct, not just plausible.
- Excellent verbal and written communication skills within a cross-functional team environment. New graduates are encouraged to apply.
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