Principal Engineer - Context Engineering & LLM Optimization
Core
Designing and optimizing context engineering strategies, prompt architectures, and retrieval orchestration for enterprise LLM and RAG applications to ensure high-quality, efficient AI interactions.
Role type
Principal Engineer, Context Engineering & LLM Optimization
Builds
Enterprise LLM and RAG application platforms, context management systems, and evaluation frameworks.
Domain
Financial services, Large Language Models (LLM), Retrieval-Augmented Generation (RAG)
Deliverable
production ML models
Required skills
LLM architecture design, context window management, prompt engineering, retrieval orchestration, RAG system design, vector search, automated LLM evaluation, enterprise architecture leadership, multi-agent workflow design, token optimization
Preferred skills
Agentic workflows, prompt injection mitigation, long-context model optimization, user personalization patterns, cloud-native engineering, distributed systems design, data governance
Technologies
OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta Llama, vector databases, semantic search tools
Responsibilities
Define prompt architectures for system, user, and retrieved contexts; optimize context window usage via summarization and compression; design retrieval orchestration patterns for data injection; develop LLM evaluation frameworks for quality and safety; establish enterprise standards for prompt and context engineering; mentor senior engineers and lead cross-functional architecture decisions.
Seniority
Principal, hands-on IC with strategic influence