I build autonomous AI systems that actually run in production — vision-based layout detection, voice agents, graph AI, multi-agent orchestrators, RAG pipelines, and the infrastructure that keeps them alive. Moving beyond wrapper agents into deep system integration — Graph DBs, dynamic SQL, computer vision. Not demos. Deployments.
const voiceAgent = async () => {
const agent = new AdvancedAIAgent({
model: 'gpt-4o-realtime',
voice: 'neural-tts-streaming',
tools: [intelligentCRMSync, contextualMemory],
memory: new MultiModalConversationMemory(),
});
await agent.connect(twilioStream);
return agent;
};
const ragEngine = new AdvancedRAGPipeline({
embeddings: huggingFaceEmbeddings,
vectorStore: pineconeVectorDB,
llm: openai.gpt4Turbo,
strategies: ['semantic-search','chunk-reranking']
});
const report = await ragEngine.synthesize({
languages: ['en','fr','nl','de','es','pt'],
});
const agent = new MultiStrategyIntelligentAgent({
crm: hubspotAPI,
llm: openai.gpt4Turbo,
automation: n8nWebhookOrchestrator,
strategies: ['intent-classification',
'predictive-scoring'],
});
await agent.processWithIntelligence(query)
.executeWorkflow();
const rag = new MultiStageRAGAgent({
vectorDB: pineconeMultiIndex,
embeddings: huggingFaceSemanticModel,
memory: persistentConversationalMemory,
strategies: ['semantic-chunking',
'hierarchical-retrieval'],
});
await rag.evaluateDocumentsConsecutively(query)
.synthesizeResponse();
model = YOLOv11(weights='layout-detect-v3.pt') results = model.predict(pdf_image, conf=0.45) regions = extract_regions(results, strategy='bbox-merge') embeddings = sentence_transformer.encode(regions) labels = lightSVM.classify(embeddings) return semantic_sections(labels, regions)
agent = PreScreeningAgent({
llm: openai.gpt4,
voice: elevenLabsAPI,
decision_engine: HiringDecisionModel(),
output: webhookDispatcher,
})
result = await agent.conduct_interview(candidate)
.evaluate_transcript()
.dispatch_decision()
engine = DMLBrainEngine({
vector_search: hybridSearchLayer,
sql_generator: DynamicSQLEngine(),
data_source: operationalDB,
})
records = engine.retrieve(query, mode='hybrid')
result = engine.inject_to_agent(agent, records)
graph_agent = Neo4jAIAgent({
driver: neo4j.driver(NEO4J_URI),
llm: openai.gpt4,
node_schema: EntityRelationshipSchema,
})
graph = graph_agent.map_entities(job_data, skill_data)
path = graph_agent.traverse(src='python', dst='ml-engineer')
Open to senior AI engineering roles, ambitious freelance builds, and anyone who wants to put real agents into production. Mention EASTEREGG in the terminal for something special.