Principal Engineer - Context Engineering & LLM Optimization
USD 122K-180K (estimate) Senior-level Full Time
Tasks
- Communicate AI and data engineering concepts to stakeholders
- Create LLM evaluation frameworks
- Create reusable reference architectures and guardrails
- Define grounding and citation handling strategies
- Define program portfolio engineering approach
- Define reusable agent and tool calling patterns
- Design prompt architectures
- Design retrieval orchestration patterns
- Develop multi turn conversation memory patterns
- Establish prompt engineering and context engineering standards
- Evaluate LLM model behavior across context sizes
- Improve data ingestion formatting and chunk structures
- Lead LLM application technical reviews
- Lead enterprise LLM context engineering strategies
- Mentor senior engineers and influence technical direction
- Optimize context window usage
- Own architecture decisions across multiple teams
- Tune RAG retrieval outputs
Perks/Benefits
Skills/Tech-stack
Agent workflows | Anthropic | Automated Evaluation | Azure OpenAI | Citation Handling | Context compression | Context window | Context window management | Deduplication | Document chunking | Embeddings | Filtering | Function Calling | Google Gemini | Grounding | Hallucination reduction | Instruction design | Jailbreak Resistance | LLM | Language Model | Large Language Model | Long Context | Long Context Models | Meta LLAMA | OpenAI | Prompt Regression | Prompt Regression Testing | Prompt engineering | Prompt injection | Prompt injection mitigation | Prompt templates | Quality Measurement | RAG | Ranking | Regression testing | Reranking | Retrieval Orchestration | Semantic Search | Summarization | System prompts | Token budgeting | Tool-Calling | Vector Search | Window management
Education
Bachelor of Applied Mathematics | Bachelor of Engineering | Bachelor of Science | Bachelor of Science in Computer Science
Roles
Architect | Engineer | Principal | Principal Engineer | Software Architect
Regions
Countries
States
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