Principal Engineer - Context Engineering & LLM Optimization
Charlotte, United States
USD 122K-180K (estimate) Senior-level Full Time
Tasks
- Communicate technical concepts to stakeholders
- Create LLM evaluation frameworks and test plans
- Define engineering approach for program portfolio solutions
- Define enterprise LLM context engineering strategies
- Define multi turn conversation memory and session state
- Define reusable agent tool calling patterns
- Design grounding citation and hallucination reduction strategies
- Design prompt architectures and prompt safety
- Design retrieval orchestration patterns for RAG
- Establish prompt engineering and context engineering standards
- Evaluate LLM behavior across prompt and retrieval strategies
- Improve data ingestion formatting and chunking
- Lead architecture decisions across multiple teams
- Lead solution design across multiple teams
- Mentor senior engineers and influence technical direction
- Optimize context window usage and token budgeting
- Oversee technical design reviews and code quality
- Tune retrieval outputs for downstream reasoning
Perks/Benefits
- Career development
- Collaboration culture
- Flexible benefits
- Inclusive workplace
- Recognition for performance
Skills/Tech-stack
Agent workflows | Automated testing | Azure OpenAI | Citations | Compliance | Context compression | Context window | Context window management | Cost Management | Embeddings | Function Calling | Grounding | Hallucination reduction | Java | LLM | LLM Evaluation | OpenAI | Prompt Regression | Prompt Regression Testing | Prompt Safety | Prompt engineering | Prompt templates | Python | RAG | Regression testing | Reranking | Retrieval Orchestration | Scalability | Security | Semantic Search | Summarization | Token budgeting | Tool use | Tool-augmented reasoning | TypeScript | Vector Search | Window management
Education
Roles
AI | AI Engineer | Architect | Engineer | Principal | Principal Engineer | Software Architect
Regions
Countries
States
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