Amin Zayeromali
Principal AI Architect · Full Stack Data Scientist · Senior System Architect

Architecting Model-Agnostic AI Infrastructure, Agentic RAG Systems, and High-Performance Backend Engineering at Scale

   

Senior System Architect and Full Stack Data Scientist with 10+ years of experience engineering production-grade AI systems across healthcare, fintech, and enterprise SaaS. Specializing in Agentic RAG Systems, Model-Agnostic AI Infrastructure, custom Knowledge Graph engineering, and high-throughput data pipelines deployed at scale on AWS and GCP.

Currently leading the architecture of a distributed microservice platform processing 2M+ monthly LLM queries with full observability, structured output enforcement via Pydantic validation, and zero vendor lock-in through a universal adapter layer. Patent pending on domain-specific retrieval-augmented conversational agents (US Patent App, Filed Sep 2025).

Key engineering outcomes: 70% improvement in semantic search accuracy · 60% gain in data privacy through federated learning · 85% boost in clinical forecast accuracy · 99.9% system uptime across production deployments.

Designing hierarchical retrieval architectures, model-agnostic LLM abstraction layers, and agentic chunking strategies for enterprise-scale knowledge systems.

Agentic RAG (LangChain · LangGraph) · Model-Agnostic Universal Adapter · Hierarchical Retrieval (Topic → Dataset → Chunk) · LLM Observability (LangSmith · Arize · Phoenix) · Prompt Engineering (Structured Outputs · HITL · CoT · Few-shot) · Federated Learning · Vector Database Optimization (Pinecone · LlamaIndex)

Building event-driven, high-availability backend systems with async Python patterns, strict data contracts, and cloud-native deployment across AWS and GCP.

Python (Async Patterns) · FastAPI · Django & DRF · PostgreSQL · Redis · Elasticsearch · Event-Driven Architecture (Kafka) · Docker · Nginx · uWSGI · AWS (EC2 · Lambda · SageMaker · Fargate) · GCP · CI/CD Automation · 99.9% Uptime Systems

Engineering ETL/ELT pipelines, custom graph traversal algorithms on Elasticsearch, and ontology-driven schema modeling for converting unstructured data into structured knowledge.

ETL/ELT Pipeline Architecture · Custom Graph Traversal Algorithms · Semantic Relation Mapping · Ontology Design & Schema Modeling · Unstructured & Multi-modal Data Processing (OCR · PDF · Blueprints) · Elasticsearch Index Engineering

Applying deep learning, NLP, and statistical modeling to production workloads including time-series forecasting, semantic search, and BERT-based text summarization.

NLP & Semantic Search · TensorFlow · PyTorch · Scikit-learn · HuggingFace Transformers · Time-Series Forecasting (LSTM) · Predictive Modeling · BERT Extractive Summarization · DBSCAN · K-Means · Hierarchical Clustering

Leading cross-functional engineering teams in designing distributed microservice architectures, enforcing domain-driven design, and establishing MLOps pipelines for production AI workloads.

Distributed Microservices Architecture · Domain-Driven Design · Design Patterns (MVC · Decorator · TMP) · Cross-Functional Team Leadership · Agile · Scrum · Kanban · MLOps Best Practices

Eliminates vendor lock-in by providing a universal interface for swapping backend LLM providers in production without rewriting downstream business logic.

Architectural Highlights:

• Adapter Pattern with Strict Data Contracts — Enforces upstream/downstream interface boundaries, enabling hot-swapping between OpenAI, Anthropic, and open-source models behind a single API surface with zero downstream code changes.
• Decoupled Routing Logic — Isolates model selection, prompt formatting, and response normalization into independent, unit-testable modules. Each LLM provider is a pluggable dependency, not a structural coupling.
• Unified Error Taxonomy — Wraps provider-specific exceptions into a normalized error hierarchy, preventing cascade failures across the inference pipeline and enabling consistent retry/fallback strategies.

Tech Stack:  Python · REST API Design · Provider Abstraction · Structured Output Contracts

Self-learning AI system that continuously assimilates and refines knowledge from heterogeneous data sources — a foundational architecture for agentic AI systems that operate without human curation loops.

Architectural Highlights:

• Self-Directed Ingestion Pipeline — Orchestrates multi-source data acquisition with automatic quality filtering, deduplication, and semantic boundary detection before knowledge integration.
• Modular Knowledge Enrichment — Decouples acquisition, validation, and refinement into discrete pipeline stages, allowing each stage to scale and evolve independently without cross-stage regressions.
• Feedback-Driven Refinement Loop — Downstream accuracy metrics propagate back to calibrate upstream data selection heuristics, creating a continuously improving knowledge base.

Tech Stack:  Python · NLP · Knowledge Graph Patterns · Pipeline Architecture

Production-oriented semantic analysis engine applying retrieval-augmented generation to extract structured, verifiable insights from unstructured legal corpora at scale.

Architectural Highlights:

• Domain-Tuned Retrieval Pipeline — Implements context-aware chunking strategies optimized for legal document structures (clauses, sections, cross-references), preserving semantic boundaries that generic splitters destroy.
• Precision Re-Ranking Layer — Stacks vector similarity retrieval with domain-specific re-ranking logic to surface contextually relevant passages over superficially similar text, reducing false-positive context injection.
• Structured Output Enforcement — Validates all generated analysis against predefined Pydantic schemas, ensuring downstream consumers receive deterministic, parseable results rather than free-form LLM output.

Tech Stack:  Python · RAG Architecture · NLP · Elasticsearch · Semantic Search · Document Processing

Computes semantic relevance scores across legal and regulatory corpora — moving beyond brittle keyword matching into embedding-driven, meaning-based retrieval and classification.

Architectural Highlights:

• Embedding-Driven Relevance Scoring — Replaces keyword-based classification with dense vector similarity computation, capturing contextual meaning across complex regulatory language with measurably higher precision.
• Horizontally Scalable Classification Pipeline — Decouples text preprocessing, embedding generation, and classification into independent stages with clean data handoffs, enabling horizontal scaling of compute-intensive steps.
• Cross-Domain Transfer Architecture — The semantic classification core is designed for portability: re-targetable to medical guidelines, compliance policies, or any domain with structured rule hierarchies without retraining.

Tech Stack:  JavaScript · NLP · Semantic Analysis · Classification Algorithms · Modular Pipeline Design

I build AI systems that ship to production and stay running — not just pass a demo. Every architecture decision is evaluated against three constraints: reliability under sustained load, clean separation of concerns across module boundaries, and the ability to swap any component — from the LLM provider to the vector store — without propagating changes through the system.