AI Engineer, Multimodal LLMs

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.