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 stores (Qdrant Pinecone Weaviate) similarity scoring reranking
Embedding models - OpenAI text-embedding-* Cohere open-source (BGE E5)
Agent frameworks - LangChain LlamaIndex Semantic Kernel or custom orchestration
Model Context Protocol (MCP) - tool exposure resource patterns for LLM agents
Agentic workflows - multi-step reasoning tool use function calling thinking/reflection loops
Engineering
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
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 stores (Qdrant Pinecone Weaviate) similarity scoring reranking
Embedding models - OpenAI text-embedding-* Cohere open-source (BGE E5)
Agent frameworks - LangChain LlamaIndex Semantic Kernel or custom orchestration
Model Context Protocol (MCP) - tool exposure resource patterns for LLM agents
Agentic workflows - multi-step reasoning tool use function calling thinking/reflection loops
Engineering
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
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