AI Engineer
CORSAIR Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at CORSAIR. Viet Nam · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Deliver AI and machine-learning features across multiple platforms, from initial build through ongoing support
- Connect models to products, selecting or replacing providers as requirements shift
- Build LLM workflows and orchestration, including retrieval systems and the data that supports them
- Test model responses, then refine accuracy, reliability, runtime cost and latency
- Assess new AI tools and frameworks and introduce useful options
What you bring
- At least four years in software engineering, with strong Python ability
- Hands-on production work with LLMs, vector stores, retrieval-augmented generation and agent frameworks
- Practical MLOps experience deploying and monitoring AI systems in production
- A working understanding of cost and speed trade-offs in large-scale LLM systems
- Fluent written and spoken English
Who this fits
This role suits an engineer comfortable working closely with a small core engineering team. It is based in Viet Nam.
From the employer
Position Summary, Responsibilities and Expectations:
• Own AI/ML feature development across multiple platforms, end to end.
• Handle model integration, including choosing and swapping providers as needs change.
• Build and maintain LLM pipelines and orchestration logic.
• Set up retrieval systems and the data logic that feeds them.
• Run evaluation on model output and iterate to improve accuracy, reliability and cost.
• Optimize performance, including cost and latency trade-offs in production.
• Track emerging AI tools and frameworks and bring in what’s worth adopting.
Essential Skills and Experience:
• 4+ years of software engineering experience.
• Strong Python skills.
• Production experience with LLMs.
• Experience with vector stores and retrieval-augmented generation.
• Experience with agentic frameworks.
• Practical MLOps experience, including deploying and monitoring AI systems in production.
• A working grasp of cost and latency trade-offs when running LLM-based systems at scale.
• Comfortable working alongside a small core engineering team.
• Fluent English, both in written and speaking.