I'm interested in designing and implementing production-ready AI systems with focus on cost-effective LLM deployments, enterprise-grade RAG solutions, and measurable business automation.
LLM Implementation & Optimization
- Production Deployment: API integration, latency optimization, cost reduction
- Enterprise Fine-tuning: Domain adaptation, RLHF implementation, quantization techniques
- Prompt Engineering: System design, guardrail implementation, evaluation frameworks
- Architecture: Scalable retrieval systems, distributed vector databases, hybrid search
- Performance: Chunking strategies, embedding optimization, context window management
- Integration: API development, middleware solutions, legacy system connections
- Document Intelligence: Extraction, classification, summarization, compliance validation
- Workflow Orchestration: Multi-agent systems for complex business processes
- Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI
- Infrastructure: AWS/Azure/GCP AI services, Docker, Kubernetes
- Evaluation: RAGAS, TruLens, LangSmith
- Vector Databases: Pinecone, Weaviate, Qdrant, Chroma
- Languages: Python
Open to discussing enterprise AI implementation challenges and solutions.
- Email: KazKozDev@gmail.com
- Pronouns: he/him