Backend development - Go Python or TypeScript/Node API design - REST and GraphQL; integrating LLM responses into structured APIs Event-driven architecture - async pipelines for ingestion enrichment and inference Graph databases - Neo4j for knowledge graphs and dependency mapping Vector databases - Qdrant pgvector or equivalent Containerisation - Docker Kubernetes; deploying inference workloads Observability - tracing LLM calls latency token usage quality metrics
Data & Context
Data ingestion pipelines - structured and unstructured source processing Knowledge graph construction - entity extraction relationship mapping Context window management - chunking summarisation compression strategies Source attribution and citation - grounding responses in verifiable data
AI Engineer Onsite 4 days each week in Pittsburg PA or Memphis TN Core AI & ML LLM integration (OpenAI Anthropic Claude Gemini) - API usage prompt engineering context management Retrieval-Augmented Generation (RAG) - vector search chunking strategies embedding pipelines Semantic search - vector s...
AI Engineer
Onsite 4 days each week in Pittsburg PA or Memphis TN