Asteri AI
AI & ML Engineer
About this role
Asteri seeks an AI & ML Engineer to design and deploy production-grade AI systems for their work intelligence platform. You'll build LLM-based applications, RAG pipelines, and agentic systems while collaborating with engineering teams to deliver reliable, scalable enterprise solutions.
What you'll do
- Design and operate production AI/ML systems powering the orchestration platform
- Deploy and iterate on LLM applications, optimizing quality, latency, and cost
- Own retrieval and agentic systems end-to-end, including RAG pipelines and workflow agents
- Define rigorous evaluation, testing, and production monitoring for AI systems
- Implement engineering best practices including CI/CD, versioning, and rollback strategies
- Collaborate with cross-functional teams to translate product requirements into robust AI solutions
What they're looking for
- Python and production software development
- Machine learning fundamentals and model evaluation
- LLM deployment and operations in production
- Cloud-based ML system deployment
- Systems design and performance optimization
- RAG systems and optimization techniques
- Testing, CI/CD, and software engineering practices
- Technical communication and complex problem-solving
Benefits
- Direct ownership of critical AI systems
- Real production impact at enterprise scale
- Remote-friendly culture
- Competitive compensation
- Opportunity to shape safe AI deployment alongside humans
- Pragmatic, production-first approach to AI development
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Asteri AI
Asteri AI builds an AI-native work intelligence platform that orchestrates enterprise workflows through LLM-based applications, RAG pipelines, and agentic systems. The company is hiring QA Automation Engineers, AI & ML Engineers, and Full Stack Engineers to develop and scale reliable, production-grade systems for large enterprise customers.
View all jobs at Asteri AILikely interview questions
- Tell us about a production ML or LLM system you've deployed. What were the key challenges in getting it to work reliably at scale, and how did you measure success?
- Walk us through how you would design a RAG pipeline for an enterprise application. What evaluation metrics would you use, and how would you optimize for both quality and latency?