Sr AI Context Engineer

• 5+ years of experience in backend software engineering, applied machine learning/search, or data-centric application development.
• GenAI & RAG Focus: 1–2 years of hands-on experience specifically optimizing AI and RAG architectures, prompt context strategies, and vector retrieval pipelines for LLM applications.
• Programming & Tooling: Advanced proficiency in Python and SQL, alongside familiarity with modern orchestration tools (e.g., Airflow, Prefect, Temporal).
• Unstructured Content Parsing: Deep experience using parsing frameworks (e.g., LlamaIndex, Unstructured.io, LangChain document loaders) to process complex layouts, tables, and unstructured documents.
• Vector & Search Engines: Hands-on experience working with vector databases, embeddings, and semantic search platforms (e.g., Pinecone, pgvector, Weaviate, Qdrant, Azure AI Search).
• Evaluation Frameworks: Familiarity with RAG and LLM context evaluation frameworks (e.g., Ragas, TruLens, Arize Phoenix) to measure retrieval recall, precision, and groundedness.
• Data Security & Privacy: Practical experience handling sensitive healthcare data (PHI/PII) within high-compliance software environments.

• Must have the ability to provide a non-cellular High Speed Internet Service such as Fiber, DSL, or cable Modems for a home office.
• A minimum standard speed for optimal performance of 30x5 (30mpbs download x 5mpbs upload) is required.
• Latency (ping) response time lower than 80 ms
• Hotspots, satellite and wireless internet service is NOT allowed for this role.
• A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information

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