Llm Trainer Jobs in Riverside, CA (NOW HIRING)

Applied Medical is a new generation medical device company with a proven business model and commitment to innovation fueled by rapid business growth and expansion. Our company has been developing and manufacturing advanced surgical technologies for over 35 years and has earned a strong reputation for excellence in the healthcare field.  Our unique business model, combined with our dedication to delivering the highest quality products, enables team members to contribute in a larger capacity than is possible in typical positions.

We are seeking a highly skilled AI Engineer with deep expertise in Large Language Models (LLMs), AI agent frameworks, and Model Context Protocols (MCP). The ideal candidate will design, develop, and deploy advanced AI-powered applications that leverage LLMs for reasoning, planning, and autonomous decision-making. In this role, you will build agentic systems capable of performing multi-step tasks, interacting with external systems, integrating with APIs and tools, and orchestrating complex workflows end-to-end. You will also implement and optimize MCP-based architectures to ensure reliable tool usage, persistent context management, and robust coordination between agents and backend services.  

Collaboration is a fundamental part of our organization's culture and is essential to our continued success. As such, the successful candidate for this position is expected to work on-site, enabling them to engage fully with colleagues and contribute to cross-functional initiatives. Therefore, the ability to work collaboratively and contribute to a positive and supportive team environment is a key requirement for this role. 

Key Responsibilities: 

• LLM Application Development: Design, fine-tune, and deploy LLM-based applications for reasoning, planning, retrieval, and automation. 

• Agent Systems: Build autonomous AI agents that interact with APIs, databases, and tools using frameworks like LangChain, LangGraph, AutoGen, or custom solutions. 

• Context & Memory Management: Implement Model Context Protocols (MCP) for persistent context, robust coordination between agents, and seamless tool usage. 

• Data & RAG Pipelines: Develop RAG pipelines, vector databases, and prompt-engineering strategies to enable context-aware responses. 

• Backend & Infrastructure: Build scalable backend systems and deploy solutions on cloud or on-prem environments (Azure, AWS), with containerization (Docker, Kubernetes). 

• Research & Innovation: Stay up-to-date with emerging LLM and agentic technologies and evaluate them for practical applications. 
• Collaboration & Governance: Work closely with data science, product, and engineering teams while ensuring responsible AI practices, model safety, and compliance with data privacy regulations. 

This position requires the following skills and attributes: 

• Bachelor’s or Master’s in Computer Science, AI/ML, or related field (Ph.D. preferred). 

• 2+ years building and deploying AI applications with LLMs (OpenAI, Anthropic, HuggingFace). 

• Strong Python skills, with experience in Git, FastAPI/Flask, and ML frameworks (PyTorch, TensorFlow). 

• Hands-on experience with agent frameworks (AutoGen, LangGraph, CrewAI) and orchestration strategies. 

• Proficiency in vector databases (Pinecone, Milvus, ChromaDB, FAISS) and embedding techniques. 

• Solid understanding of prompt engineering, RAG, and LLM optimization. 

• Familiarity with cloud/on-prem deployment and containerization (Docker, Kubernetes). 

• Experience implementing Model Context Protocols for context management and interoperability. 

• Experience with multi-agent systems, planning algorithms, or reinforcement learning with LLMs. 

• Knowledge of production-grade AI monitoring, logging, and observability. 

• Excellent problem-solving skills and ability to thrive in fast-paced, collaborative environments.

• Competitive compensation range: $80000 - $140000 / year (California).
• Comprehensive benefits package.
• Training and mentorship opportunities.
• On-campus wellness activities.
• Education reimbursement program.
• 401(k) program with discretionary employer match.
• Generous vacation accrual and paid holiday schedule.

Please note that the compensation range may be adjusted in the future, and bonus and incentive compensation plans may apply.

Our total reward package reflects our commitment to employee growth and well-being, as we invest in your development and offer a range of benefits designed to enhance your career and life.

All compensation and benefits are subject to plan documents and written agreements.

Equal Opportunity Employer

Applied Medical is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (including pregnancy, childbirth, breastfeeding and related medical conditions), or sexual orientation, or any other status protected by federal, state or local laws in the locations where Applied Medical operates.

This position requires the following skills and attributes: 

• Bachelor’s or Master’s in Computer Science, AI/ML, or related field (Ph.D. preferred). 

• 2+ years building and deploying AI applications with LLMs (OpenAI, Anthropic, HuggingFace). 

• Strong Python skills, with experience in Git, FastAPI/Flask, and ML frameworks (PyTorch, TensorFlow). 

• Hands-on experience with agent frameworks (AutoGen, LangGraph, CrewAI) and orchestration strategies. 

• Proficiency in vector databases (Pinecone, Milvus, ChromaDB, FAISS) and embedding techniques. 

• Solid understanding of prompt engineering, RAG, and LLM optimization. 

• Familiarity with cloud/on-prem deployment and containerization (Docker, Kubernetes). 

• Experience implementing Model Context Protocols for context management and interoperability. 

• Experience with multi-agent systems, planning algorithms, or reinforcement learning with LLMs. 

• Knowledge of production-grade AI monitoring, logging, and observability. 

• Excellent problem-solving skills and ability to thrive in fast-paced, collaborative environments.