AI Engineer, Multimodal LLMs

Your Role As an AI Engineer at Eloquent AI, you will be at the forefront of building, deploying, and optimizing enterprise-grade AI agents that handle high-stakes conversations. You’ll work directly with software engineers, deep learning experts and AI researchers to design intelligent agents that understand, take action, and deliver real business impact. This role requires a mix of software development, AI integration, and problem-solving skills, with the ability to customize, optimize, and scale AI models for real-world enterprise applications. If you're passionate about LLMs, conversational AI, and solving last-mile AI adoption challenges, this role is for you. You will: 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. 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. #J-18808-Ljbffr