# AI Engineer, Multimodal LLMs

**Company:** [Eloquent AI](https://hotfix.jobs/companies/eloquent-ai)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Experience:** 3+ years
**Skills:** Python, PyTorch, TensorFlow, LLMs, Fine-Tuning, Inference Optimization, AWS, GCP, Azure, Prompt Engineering, Peft, RAG, Reinforcement Learning, Computer Vision, NLP
**Posted:** 2025-08-22

> Builds and optimizes multimodal LLM-based AI agents for enterprise conversations, integrating with systems via APIs and automating high-stakes workflows. Requires 3+ years in AI engineering, Python/PyTorch proficiency, and experience with LLMs or vision models.

## Job Description

## Responsibilities
- Build, deploy, and optimize AI agents that engage in enterprise-grade conversations.
- Design & develop next-gen multimodal LLM architectures (LLMs, speech, vision, reinforcement learning).
- Explore optimal trade-offs between model quality and efficiency when translating research into practical solutions.
- Refine training paradigms for real-world applications.
- Integrate AI agents with enterprise systems via APIs, databases, and automation tools.
- Experiment rapidly to improve AI-driven interactions, response quality, and automation capabilities.
- Collaborate with cross-functional teams (engineering, research, and product) to shape Eloquent AI’s roadmap.
- Monitor and improve agents’ performance via user simulations and evaluations.

## Requirements
- 3+ years of experience in software development, AI engineering, or NLP in a production environment.
- Strong proficiency in **Python**, with experience in frameworks like **PyTorch** and **TensorFlow**.
- Experience working with LLMs or large computer vision models, or generative AI models, including fine-tuning and inference optimization.
- Familiarity with APIs, cloud infrastructure (**AWS**, **GCP**, or **Azure**), and enterprise integrations.
- Ability to prototype, experiment, and iterate quickly to improve AI agents.
- Strong problem-solving skills and the ability to work closely with customers to refine AI solutions.
- Solid mathematical foundation of machine learning and deep learning techniques.

## Nice-to-Haves
- Experience with prompt engineering, parameter-efficient fine-tuning (**PEFT**), retrieval-augmented generation (**RAG**), reinforcement learning for LLMs.
- Published AI research in top tier AI conferences like: NeurIPS, ACL, SIGIR, ICML and ICLR.
- Contributed to open-source NLP projects.
- Worked in a fast-paced startup environment and thrive in rapid iteration cycles.

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