LLM Engineers

Job Overview

Master Works is looking for a highly skilled LLM Engineer to join our AI Core Delivery team within the AI & Analytics CoE. In this role, you will develop, deploy, and optimize advanced language model capabilities, including Retrieval-Augmented Generation (RAG) pipelines and agentic AI systems. You will play a key role in scaling AI Core as the central enterprise AI platform by ensuring production‑ready performance, robust observability, and seamless integration of LLMs with internal and external systems.

• Model Development & Optimization
  • Build and optimize RAG pipelines for efficient document processing, indexing, and retrieval
  • Implement agentic AI logic for tool integration, task automation, and external data access
  • Enhance query understanding, response ranking, and AI‑generated output quality
  • Develop evaluation benchmarks to measure LLM performance, accuracy, and relevance
• LLMOps & Deployment
  • Collaborate with API developers and backend engineers to deliver secure, observable LLM endpoints
  • Deploy and manage LLMs in cloud‑native or hybrid environments with support for scalability and multi‑cloud readiness
  • Implement CI/CD workflows for model updates, rollback mechanisms, and performance tuning
• Backend & Data Integration
  • Integrate LLM capabilities with enterprise data sources and knowledge bases
  • Support multi‑source retrieval and embedding strategies to improve AI responses
  • Design pipelines that ensure low latency, high throughput, and fault tolerance
• Security & Compliance
  • Apply enterprise‑grade authentication, access management, and encryption standards
  • Ensure compliance with internal governance policies, data privacy, and audit requirements
• Continuous Improvement
  • Monitor system performance, debug issues, and fine‑tune models based on real‑world feedback
  • Stay updated on advancements in LLM architectures, open‑source tools, and evaluation frameworks

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field
• Hands‑on experience with LLM development and deployment (OpenAI, Anthropic, Hugging Face, LLaMA, etc.)
• Strong understanding of Retrieval‑Augmented Generation (RAG), embeddings, and vector databases
• Experience with cloud environments (AWS, Azure, GCP) and infrastructure‑as‑code (IaC)
• Proficiency with Python or similar languages for AI/ML pipeline development
• Familiarity with container orchestration (Docker, Kubernetes) and CI/CD practices
• Knowledge of observability tools for monitoring AI performance in production environments

• Experience integrating agentic AI capabilities with external APIs and automation tools
• Strong grasp of prompt engineering, contextual memory systems, and LLM evaluation
• Exposure to enterprise AI security and compliance standards