AI Engineer, Multimodal LLMs
San Francisco • FullTime
Posted 1y ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Agent Engineer
About the job
Eloquent AI is building the next generation of AI Operators—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology transforms how work gets done in regulated, high-stakes environments, powering leading financial institutions and insurers. We are a fast-growing company backed by top-tier investors, seeking passionate individuals to redefine the future of financial services. As an AI Engineer, you will be at the forefront of building, deploying, and optimizing enterprise-grade AI agents that handle high-stakes conversations, working directly with software engineers, deep learning experts, and AI researchers. 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.
Responsibilities
- Build, deploy, and optimize AI agents for enterprise-grade conversations.
- Design and develop next-generation multimodal LLM architectures (LLMs, speech, vision, reinforcement learning).
- Explore trade-offs between model quality and efficiency for 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 to shape the company'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, 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 ability to work closely with customers to refine AI solutions.
- Solid mathematical foundation of machine learning and deep learning techniques.