Principal AI/ML Architecture

Principal AI/ML
Architect & Engineer.

GenAI Applications · MLOps · Intelligent Workflows

I design and build governed GenAI and ML systems that connect retrieval, tools, models, workflows, and human decisions — then make them evaluable, observable, secure, and operable in production.

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Production GenAIRetrieval-augmented investigation workflows shipped in a regulated enterprise setting

Governed RAGHybrid retrieval, reranking, and source-grounded synthesis with citations

Agentic WorkflowsBounded, supervised multi-agent orchestration with human approval

Secure AI DeliveryIdentity, authorization, and observability engineered at every boundary

Selected AI systems

Recent systems that show how I architect AI beyond the model.

I focus on the full decision system: retrieval quality, bounded reasoning, deterministic controls, security boundaries, evaluation, observability, resilience, and human accountability.

Production • Regulated financial services

Investigator assistant for elder financial exploitation

A governed investigation-assistance system that turns case signals and enterprise policy knowledge into evidence-backed, cited decision support while keeping final disposition with a human investigator.

Case intake→ Semantic signals→ Governed retrieval→ Cited synthesis→ Verification→ Human review

Grounded RAGHybrid retrieval, metadata-aware eligibility, multi-query evidence gathering, reranking, provenance, and citations.

Deterministic controlsAuthorization, state transitions, evidence validation, bounded repair, idempotency, retries, and fail-closed checks.

Durable workflowAsynchronous processing, persisted checkpoints, reviewer queue, resumable human-in-the-loop decisions, and audit history.

Quality & operationsOffline retrieval/generation evaluations, runtime verification, tracing, failure diagnostics, and controlled feedback loops.

Synthetic portfolio case study • Production-shaped

Premium exception investigator — bounded multi-agent workflow

A deliberately synthetic P&C premium-billing investigation system built to explore how specialized agents can collaborate safely across structured evidence, relationship graphs, policy knowledge, and human approval.

Fictional insurer, synthetic corpus, and no real-money movement. The case study exists to make architecture choices and tradeoffs inspectable.

Event→ Supervisor→ Evidence / Relationship / Policy→ Resolution→ Compliance critic→ Human approval

Multi-agent + GraphRAGLangGraph orchestration with bounded specialist agents, Qdrant retrieval, Neo4j relationship expansion, and typed contracts.

Evaluation-firstGolden cases, retrieval comparisons, grounding checks, deterministic validators, and workflow-level quality gates before adding autonomy.

Security & guardrailsKeycloak identity, scoped tool permissions, source trust boundaries, schema validation, authority checks, and bounded revision loops.

Observability & HITLLangfuse traces, durable checkpoints, audit history, approval/rejection/correction paths, and explicit escalation for high-risk decisions.

How I design production AI

Judgment first, tools second.

Use an LLM where semantic reasoning adds value, and deterministic software where correctness must be enforced. Use agents only where specialization or independent reasoning earns its complexity.

01 / RETRIEVAL

Governed Retrieval & RAG

Hybrid dense and lexical retrieval, metadata-aware filtering, reranking, and source-grounded synthesis with citations. Graph retrieval when relationships are the problem — not by default.

02 / ORCHESTRATION

Bounded Agentic Workflows

Supervisor-coordinated agents with specialized responsibilities, deterministic routing where appropriate, structured outputs, and bounded retries.

03 / QUALITY

Evaluation & Guardrails

Golden-case evaluation, groundedness and citation checks, and deterministic assertions where correctness can be checked deterministically — LLM-as-judge only where it's genuinely needed.

04 / SECURITY

Security & Identity

Authorization enforced at APIs, tools, and data boundaries — not just the UI. Least privilege, service identity, and scoped access throughout.

05 / VISIBILITY

AI Observability

Trace and span visibility into retrieval, model calls, tool calls, and state transitions. Application logs alone aren't enough for AI workflows.

06 / CONTINUITY

Durable Human-in-the-Loop

Workflow state checkpoints and persists through interruption, so review can resume minutes, hours, or days later without losing context.

Engineering foundation

The delivery engineering behind the AI.

18+ years of enterprise technology delivery — from cloud data platforms and workflow orchestration to production AI systems — grounded in secure, observable application engineering.

Python & FastAPI AWS S3, Glue & Iceberg Event-Driven Systems PostgreSQL Docker Kubernetes / EKS Terraform Jenkins / CI-CD IAM, KMS & Secrets Manager APIs

Career & domain context

A path built on orchestration, state, and control.

My background runs from enterprise workflow orchestration and human-task management through enterprise architecture and cloud data platforms into production GenAI and intelligent AI workflows — carrying forward the same discipline around state, SLAs, exception handling, auditability, and deterministic control.

Financial Services Insurance Healthcare Regulated Enterprise Workflows

Credentials

Verified credentials that support the work.

Verified cloud and engineering certifications, plus focused machine-learning and deep-learning specializations that support my AI/ML architecture work.

AWS Certified Machine Learning Engineer Associate badge

AWS ML Engineer – Associate

AWS Certified Data Engineer Associate badge

AWS Data Engineer – Associate

AWS Certified Solutions Architect Associate badge

AWS Solutions Architect – Associate

AWS Developer – Associate

AWS Certified SysOps Administrator Associate badge

AWS SysOps Administrator – Associate

Certified Kubernetes Application Developer

Python Certified Associate Programmer

PySpark Certification

DeepLearning.AI & Stanford Online Machine Learning Specialization

Structured learning in supervised learning, model evaluation, and applied machine-learning workflows.

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DeepLearning.AI Deep Learning Specialization

Advanced coursework covering neural networks, deep learning foundations, and practical model-development concepts.