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Experienced AI Engineering specializing in Large Language Model (LLM) development and deployment with strong expertise in transformer architectures, fine-tuning techniques (LoRA, QLoRA), and Retrieval-Augmented Generation (RAG) systems.
Proficient in building end-to-end AI pipelines, including data ingestion, embedding generation, vector search using Qdrant, and RESTful API integration via FastAPI and Streamlit. Skilled in prompt engineering and developing multi-agent orchestration systems using LangChain for complex, knowledge-driven workflows
A seasoned Data Engineers, DevOps Engineers, and Database Architects with two decades of experience in designing, optimizing, and managing high-performance data systems. The focus is on developing reliable data pipelines, architecting scalable and resilient databases, and deploying cloud-native solutions to enable real-time analytics and enterprise-level data processing.
LLM Engineering & Prompt Design: Practical experience with OpenAI, LLaMA3, Gemma, and local model deployment via Ollama. Skilled in designing efficient prompts and dynamic AI workflows for document analysis, search, and decision-making tasks.
RAG Systems & Vector Search: Built custom Retrieval-Augmented Generation pipelines using Qdrant and various embedding models. Developed FastAPI-based services that perform real-time document querying, intelligent tagging (via KeyBERT), and semantic search.
Model Fine-Tuning & Optimization: Worked with LoRA, QLoRA, and GGUF formats to fine-tune and serve models locally. Created scripts to train and evaluate models in constrained environments, including exporting for use with Ollama.
AI Agents & Multi-Agent Systems: Designed LangChain-based multi-agent pipelines for task decomposition, coordination, and reasoning. Focused on agent orchestration for knowledge-intensive tasks.
NLP & Transformers: Experience with tokenization, embeddings, and transformer architecture (HuggingFace, PyTorch). Able to train and evaluate models for intelligent document processing and NLP automation tasks.
Deployment & APIs: Built RESTful APIs and Streamlit apps to integrate LLMs into end-user applications. Familiar with scalable deployment using FastAPI and real-time interaction with AI services.
Data Engineering & Architecture:
Proficiency in Python and SQL, with extensive experience in ETL/ELT frameworks including Debezium, Kafka, Greenplum, and Hadoop, enabling robust real-time data streaming and analytics.
Database Administration & Optimization:
Over 20 years of experience managing SQL Server, PostgreSQL, and distributed databases, with deep expertise in query optimization, high availability configurations, and disaster recovery strategies.
DevOps & Cloud Solutions:
10+ years expertise in Docker, Kubernetes, Terraform, Ansible, and cloud platforms such as AWS and GCP. Skilled in implementing infrastructure as code (IaC) and building efficient CI/CD pipelines.
Microservices & Automation:
Development of containerized, event-driven microservices and adoption of GitOps workflows for building scalable and resilient data platforms.
Performance & Security:
Advanced knowledge of indexing techniques, data partitioning, replication, and compliance with regulatory standards such as GDPR and HIPAA.
This portfolio reflects a results-oriented engineering approach focused on data-driven innovation, cloud transformation, and automation—bridging the gap between data, DevOps, and engineering excellence.