I build production AI systems that turn model capability into usable enterprise workflows: agent orchestration, fine-tuning, retrieval evaluation, explainability, analytics products, and the platform plumbing needed to keep all of it observable.
| Area | Current focus |
|---|---|
| Agent platforms | Multi-agent coordination, deterministic tool execution, human-in-the-loop confirmations, tool-call graphs, and enterprise workflow automation. |
| LLM operations | SFT, DPO, PPO, QLoRA, Axolotl, vLLM, checkpoint recovery, model serving, and failure diagnostics for production AI workflows. |
| Retrieval and evaluation | RAG/GraphRAG over long-form and graph-structured documents, ranking quality, BM25, MMR, reranking, and nDCG@k measurement. |
| Explainability and analytics | Model-agnostic attribution, PDP workflows, KPI design, DuckDB/Arrow pipelines, SHAP alignment, and stakeholder-facing analytics products. |
| Platform engineering | FastAPI services, Kubernetes reconciliation loops, productized launch flows, health checks, observability, and reusable enterprise assets. |
I care about systems that are measurable, debuggable, and useful to the people who have to run them after the demo is over.
- Architected an NVIDIA NeMo Agent Toolkit LLM runtime for dynamic reasoning chains, multi-agent coordination, deterministic tool execution, and enterprise workflow automation.
- Built a FastAPI execution layer that converts user/model intent into runnable agent configurations across tools, document workflows, prediction scoring, and human-in-the-loop approvals.
- Designed a pluggable enterprise tool registry with persisted execution metadata and tool-call graphs for observability, reproducibility, workflow reuse, and operational debugging.
- Productized an OpenClaw-based agent platform with sandbox launch flows, tenant configuration, health checks, gateway reachability checks, Slack access, streaming responses, and failure diagnostics.
- Led a fault-tolerant fine-tune-and-serve platform for enterprise AI use cases, translating customer requirements into scalable experimentation and deployment workflows.
- Implemented SFT and DPO workflows using Axolotl and QLoRA; automated checkpoint detection and recovery, reducing manual setup and monitoring effort by 80%.
- Enabled a Fortune 50 telecom client to launch a security metadata classifier on schedule through a productionized fine-tuning and serving workflow.
- Designed retrieval workflows for graph-structured and long-form enterprise documents while balancing ranking quality, token constraints, modular experimentation, and production evaluation.
- Improved retrieval nDCG@k by 25% through iterative tuning of BM25, Maximal Marginal Relevance, and reranking components.
- Led a standardized model explainability/PDP analytics workflow that reduced per-feature computation time by 17x while maintaining median curve fidelity around 0.90.
| Build | What it explores |
|---|---|
| Open Prior Auth Workbench | A FHIR-first healthcare AI workbench for discovering coverage requirements, prefilling documentation questionnaires, assembling submission-ready packets, and tracking case status through human review. |
| Multi-agent commerce systems | A Swiggy-style production multi-agent system spanning food, delivery, and dine-out domains. |
| LLM workflow tools | Obsidian/n8n LLM-wiki writing agents, MiroFish-style LLM councils for risk decisioning, GEN-1 robotics concepts, and Codex agentic OS experiments. |
Agentic AI & LLM systems
multi-agent orchestration · tool registries · AgentOps · human-in-the-loop flows · RAG · GraphRAG · SFT · DPO · PPO · QLoRA · Axolotl · vLLM · LangChain · LlamaIndex · NVIDIA NeMo Toolkit · NeMo Guardrails · OpenAI · Vertex AI
Platforms, data & backend
Python · SQL · Cypher · FastAPI · Flask · PySpark · DuckDB · PostgreSQL · MongoDB · Neo4j · Chroma · AWS · GCP · Docker · Kubernetes · GitHub Actions · OpenTelemetry · Langfuse
ML, product & analytics
PyTorch · TensorFlow · Keras · scikit-learn · LightGBM · SHAP · ONNX · PDP · KPI design · stakeholder discovery · PRDs · MVP roadmaps · success metrics
| Project | Signal |
|---|---|
| Gaming-Industry-Analysis | Data analysis of a 40-year gaming dataset, including genre/platform trends, sales patterns, publisher contributions, and a companion long-form article. |
| Prediction-of-Customer-Churn | ANN-based churn prediction for banking customers with ROC, confusion matrix, pie chart, KDE, and counter-plot analysis. |
| Disaster-Response-Pipeline-Web-App | End-to-end ETL, NLP, and ML pipeline powering a web app for classifying disaster-response messages. |
| Recommendation-of-Refactoring-Techniques-to-address-Self-Admitted-Technical-Debt | SATD detection and refactoring recommendation work from my RIT capstone. |
- The Essential Guide to Effectively Summarizing Massive Documents, Part 1
- Advancing the Power of Retrievers in RAG Frameworks
- Customer Segmentation, Identifying the Profit Among the Loose Ends.
- The Last 40 Years of Gaming Industry, Unlocked.
Auto-updated from my Medium RSS feed.
Other work: PPO post-training for Llama text-to-SQL, SATD detection and refactoring recommendation, and histopathology carcinoma classification using multi-level spatial fusion.
- Authored the core problem statement and evaluation metrics for the UC Berkeley AI Summit 2023 - Data Science Hackathon.
- Represented Aible at Ai4 2023, Google Next 2024, and AWS Summit 2024, translating technical systems into demos and customer conversations.
- Write long-form pieces on document summarization, retrieval systems, RAG evaluation, customer segmentation, applied AI, and gaming industry analysis.
These metrics summarize how I use AI coding tools in practice, not just how much AI-generated code I produce. The card is generated from sanitized local AI Engineer Coach aggregates and is meant to show AI engineering discipline across context quality, prompt clarity, review habits, tool usage, and agentic SDLC coverage.
| Metric | What it means |
|---|---|
| AI Practice Score | Overall signal of AI-assisted engineering maturity across the tracked categories. |
| Anti-pattern Rate | Number of detected AI workflow anti-patterns per 100 requests. Lower is better. |
| Resolution Rate | Share of detected anti-patterns that were improved or resolved over the measured period. |
| Context Health | How well my projects provide the context an AI agent needs: instructions, workspace structure, and agent-readiness. |
| Prompt Quality | How clearly I frame tasks, constraints, expected outputs, and review criteria for AI tools. |
| Review / Verification | How consistently AI-generated work is checked through review, testing, validation, or manual inspection. |
| Tool Mastery | How effectively I use AI tools, workflows, and coding assistants beyond simple prompt-and-paste usage. |
| Agentic SDLC Coverage | How broadly I use AI across planning, implementation, testing, review, documentation, and iteration. |
Public card only. Raw prompts, private code, workspace names, file paths, model names, screenshots, and detailed anti-pattern records are not published.
| Signal | What to look for |
|---|---|
| Languages | A practical mix of data, backend, notebooks, and web-facing work rather than a single narrow stack. |
| Repositories | Public projects skew older but show the arc from analytics and ML pipelines toward AI-native systems. |
| Writing | Medium activity makes the technical reasoning visible, especially around retrieval, summarization, and applied analytics. |
| Activity feed | Recent public GitHub events are generated below so profile movement is visible between larger project updates. |
- 🎉 Merged PR #12 in vinzlercodes/artifact-format-eval
- 🎉 Merged PR #11 in vinzlercodes/artifact-format-eval
- 🎉 Merged PR #10 in vinzlercodes/artifact-format-eval
- 🎉 Merged PR #9 in vinzlercodes/artifact-format-eval
- 💪 Opened PR #12 in vinzlercodes/artifact-format-eval
- 💪 Opened PR #11 in vinzlercodes/artifact-format-eval
- 💪 Opened PR #10 in vinzlercodes/artifact-format-eval
- 💪 Opened PR #9 in vinzlercodes/artifact-format-eval
- 🎉 Merged PR #8 in vinzlercodes/artifact-format-eval
- 💪 Opened PR #8 in vinzlercodes/artifact-format-eval
Fun fact: I will absolutely over-analyze both fragrance notes and video-game industry trends.