AI Context Engineer
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Job Description
Context Engineer designing and optimizing AI information architectures. Focused on LLMs and context management to improve AI outputs.
Responsibilities:
- Design and implement context pipelines for LLM-based systems.
- Structure information to maximize model understanding and response quality.
- Define strategies for prompt composition, context injection, and tool usage.
- Build and optimize RAG pipelines using vector databases.
- Implement document ingestion, chunking, embedding, and retrieval strategies.
- Improve retrieval precision and reduce hallucinations in AI outputs.
- Design and maintain prompt frameworks for AI agents and applications.
- Optimize prompts through systematic testing and evaluation.
- Integrate prompts with tool use, APIs, and agent workflows.
- Structure knowledge bases for AI consumption.
- Implement pipelines for data preprocessing, indexing, and embedding generation.
- Manage semantic search and knowledge retrieval systems.
- Analyze model performance and improve context efficiency.
- Monitor latency, token usage, and system scalability.
- Develop evaluation methods to measure prompt and context performance.
- Work closely with AI Engineers, Data Engineers, and Product Teams.
- Translate business requirements into AI-powered solutions.
- Document context architectures and AI workflows.
Requirements:
- Strong experience working with LLMs (OpenAI, Anthropic, open-source models, etc.)
- Experience building RAG systems
- Knowledge of vector databases (Pinecone, Weaviate, Qdrant, Chroma, etc.)
- Understanding of embeddings and semantic search
- Experience with prompt engineering and prompt evaluation
- Programming skills in Python or TypeScript
- Experience with API integrations
- Understanding of LLM limitations, hallucinations, and context windows
- Knowledge of token optimization strategies
- Familiarity with agent frameworks (LangChain, LlamaIndex, Semantic Kernel, etc.)
- Experience working with structured and unstructured data
- Knowledge of JSON, APIs, and data pipelines
- Strong analytical and problem-solving mindset
- Ability to experiment and iterate rapidly
- Clear technical documentation skills
Benefits:
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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