AGENTIC

AI Context Engineer

AGENTIC

contract

Posted on:

Location Type: Remote

Location: United States

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About the role

  • 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
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
LLMsRAG systemsvector databasesembeddingssemantic searchprompt engineeringPythonTypeScriptAPI integrationsdata pipelines
Soft Skills
analytical mindsetproblem-solvingexperimentationiterationtechnical documentation