Eloquent AI
AI Engineer, Multimodal LLMs
About this role
Eloquent AI seeks an AI Engineer to build and optimize multimodal LLM-based autonomous agents that handle complex enterprise workflows in financial services. You'll collaborate with engineers and researchers to design, deploy, and refine AI systems that integrate with enterprise infrastructure while balancing model quality with real-world efficiency constraints.
What you'll do
- Build and deploy enterprise-grade AI agents for high-stakes conversational workflows
- Design multimodal LLM architectures incorporating language, speech, vision, and reinforcement learning
- Optimize model performance and efficiency trade-offs for production applications
- Integrate AI agents with enterprise systems via APIs, databases, and automation tools
- Conduct rapid experimentation to improve agent interactions and response quality
- Monitor agent performance and collaborate with cross-functional teams on product roadmap
What they're looking for
- Python development with PyTorch and TensorFlow
- LLM fine-tuning and inference optimization
- Prompt engineering and RAG implementation
- Cloud infrastructure (AWS, GCP, Azure)
- API design and enterprise system integration
- Machine learning and deep learning fundamentals
- Rapid prototyping and experimentation
- Problem-solving and customer collaboration
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Eloquent AI
Eloquent AI builds autonomous AI agents and systems powered by large language models to handle complex enterprise workflows in financial services and insurance. The company is hiring front-end engineers, full-stack AI engineers, senior infrastructure engineers, and AI specialists to develop conversational interfaces, scalable backends, production deployment systems, and multimodal AI agents.
View all jobs at Eloquent AILikely interview questions
- Walk us through a production LLM or generative AI project you've built end-to-end—what were the key challenges in moving from research to deployment, and how did you optimize for quality vs. efficiency?
- Describe your experience fine-tuning large language models or vision models. What techniques have you used (LoRA, QLoRA, full fine-tuning) and how did you decide which was right for your use case?