Portfolio
Explore representative builds across support automation, semantic search, production inference APIs, and workflow orchestration.
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A track record across internal AI PaaS, RAG assistants, LLM deployment, MLOps, voice AI, and predictive intelligence for business-critical workflows.
Architected a Netflix/Uber-style internal AI PaaS on Kubernetes, powering 100+ simultaneous ML and GenAI projects with self-serve GPU workspaces, plugin-based RAG, inference, vector database, and training capabilities.
Designed an AI workflow for complaint intake, classification, priority detection, policy-aware response drafting, and escalation routing so support teams can resolve customer complaints faster with consistent quality.
Built an internal AI support assistant that reduced manual platform support by combining FAISS-based RAG, fixed Q&A flows, and a GitHub agent that can propose YAML config changes through pull requests.
Hosted LLaMA 3 with vLLM and Ray Serve, then integrated LlamaIndex and Weaviate for HR policy Q&A using semantic chunking, BM25, and cosine similarity retrieval.
Delivered a legal AI system with BERT classification across 75 document categories, BiLSTM and spaCy NER, plus end-to-end MLOps on GKE with ZenML, Seldon, Kubeflow, and MLflow.
Built a multi-stage AI interviewer that scores resumes against job descriptions and conducts automated voice screening with Azure OpenAI, LLaMA 3.1, and bidirectional speech services.
Developed XGBoost and ensemble models for churn risk and customer lifetime value prediction, supporting product strategy with A/B testing and projected revenue uplift opportunities.
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