Building AI that works
where it has to.
I'm a Senior AI and GenAI Engineer with 5+ years building production AI systems across financial services, healthcare, and insurance. My work lives at the intersection of LLM architectures, data engineering, and regulated deployment — where "it works in a notebook" means absolutely nothing.
I've designed RAG pipelines that serve sub-second financial document retrieval, built agentic workflows using LangGraph and MCP for multi-step document auditing, and shipped LLM-backed systems that operate under compliance constraints most engineers never have to think about. I'm a backend-first engineer: Python, FastAPI, Spark, and whatever infrastructure the problem demands.
Before GenAI was a buzzword, I was building data pipelines, classical ML models, and NLP systems. That foundation is why my LLM systems actually work in production — I know what breaks, what scales, and what auditors ask about.
Career Timeline
Senior AI and GenAI Engineer
CurrentLeading delivery of production-grade AI capabilities for finance-domain workflows. Architecting LLM-backed document processing, RAG retrieval, and agentic automation under strict regulatory constraints.
- Designed end-to-end LLM systems for financial document ingestion, classification, extraction, and summarization under compliance constraints
- Architected RAG pipelines with Weaviate achieving sub-second retrieval of financial policies and historical records via hybrid dense vector + metadata filtering
- Built agentic AI workflows using LangGraph and MCP for stateful multi-step document auditing with standardized tool integration
- Deployed intelligent routing logic for financial transaction approvals, significantly reducing manual review volume
- Integrated DeepEval quality gates into CI/CD, enforcing faithfulness and relevancy checks on LLM outputs before release
- Owned architecture decisions balancing model accuracy, latency, cost ceilings, and regulatory expectations
AI Engineer
Applied AI and data engineering role focused on Python-driven data foundations and backend workflows, transitioning into LLM evaluation and early generative AI initiatives.
- Engineered scalable Python services for processing and enriching large structured/semi-structured datasets for production AI workloads
- Led controlled experiments benchmarking GPT-style models against legacy NLP systems with custom quantitative comparison tooling
- Developed prompt structuring strategies to stabilize model outputs and reduce hallucinations during experimental LLM phase
- Supported NLP-based text processing for classification, summarization, and information extraction
- Containerized key workflows for consistent execution across environments
Data & Applied Analytics Engineer
Built data pipelines and applied analytics in healthcare, processing sensitive records under strict data handling requirements with early ML and NLP initiatives.
- Built data ingestion and transformation pipelines for sensitive structured and semi-structured healthcare records
- Implemented rule-based and classical NLP for document tagging, keyword extraction, and text classification
- Designed data models and quality checks supporting analytics, reporting, and early AI-adjacent workflows
- Enforced deterministic behavior and reproducibility due to regulatory sensitivity
- Integrated pipelines with backend systems while enforcing access controls and audit expectations
Software and Data Engineer
Backend and data-focused engineering supporting data-intensive applications, ETL workflows, and operational systems in insurance.
- Developed Python services for processing, validating, and transforming structured datasets for reporting and operational systems
- Designed and maintained ETL workflows ingesting data from multiple upstream sources
- Built and optimized SQL queries and data models for analytics and reconciliation
- Implemented validation checks and logging for upstream data quality monitoring
Technical Expertise
A production-hardened stack built across regulated industries — not a list of tutorials completed.
Applied AI & Generative AI
Machine Learning & NLP
Programming & Backend
Data Engineering & Pipelines
MLOps & Deployment
Cloud & Infrastructure
Datastores
Security & Compliance
Academic Background
Education
Certifications
360° Panoramic View Synthesis via Depth-Based Multiplane Images
Engineered a PyTorch framework for spherical view synthesis by constructing Multiplane Images from cube map projections and metric depth estimation (DepthAnything v2). Achieved comparable PSNR/SSIM scores versus a Google Research neural network MPI baseline, providing an efficient, interpretable alternative for VR/AR applications.
Let's Build
Something.
I'm open to senior AI engineering roles, technical advisory, and interesting problems in regulated AI. If you're working on something that needs production rigor, let's talk.