Ultralytics LLM Engineer

Ultralytics is a global leader in AI innovation, committed to democratizing access to transformative AI solutions. As an Ultralytics LLM Engineer, you will play a pivotal role in advancing LLM capabilities, developing innovative AI-driven solutions, and optimizing model performance.

Responsibilities:

• Researching, fine-tuning, and deploying state-of-the-art LLMs
• Developing APIs and tools to enable seamless integration of LLM functionalities
• Optimizing model performance for scalability and efficiency
• Collaborating with the team to create innovative AI-driven solutions
• Implementing automated checks for data quality and pipeline reliability
• Supporting the integration of LLMs with Ultralytics solutions and workflows
• Building internal company tools leveraging LLM capabilities
• Designing and deploying Retrieval-Augmented Generation (RAG) applications
• Enhancing query enrichment, reranking, and search relevance systems
• Ensuring compliance with SOC 2 and ISO standards
• Engaging with the open-source community to promote adoption and gather feedback
• Writing and maintaining technical documentation and guides
• Staying at the forefront of LLM advancements and sharing insights with the team

Requirements:

• 5+ years of experience with Python and AI/ML frameworks
• Extensive experience working with LLMs and transformer architectures
• Strong understanding of NLP concepts, tokenization, embeddings, and fine-tuning
• Proven experience deploying LLMs in production environments
• Expertise with APIs from Anthropic, OpenAI, Voyage, DeepSeek, and Gemini
• Experience with building internal tools leveraging LLM functionalities
• Hands-on experience designing and implementing RAG applications
• Proficiency in query enrichment and reranking methodologies
• Familiarity with SOC 2 and ISO compliance requirements
• Strong debugging and optimization skills
• Excellent written communication and documentation abilities
• Familiarity with open-source AI tools and communities
• Experience with Docker and cloud-based deployment AWS, GCP, or Azure