Forward-Deployed AI Architect

Christopher Mangun

Principal AI Platform Engineer — Enterprise AI Systems for Regulated Environments

I am a Forward-Deployed AI Architect and Principal AI Platform Engineer specializing in the design and deployment of production-grade AI systems for regulated enterprises. My work spans Retrieval-Augmented Generation (RAG), enterprise search, AI governance, MLOps, and human-in-the-loop systems, with deep experience in healthcare, life sciences, and compliance-driven environments.

I have led AI initiatives from ambiguous discovery through production delivery, audit readiness, and organizational adoption—ensuring systems are reliable, explainable, secure, and compliant.

How I Work

I operate as a Forward-Deployed Engineer embedded with stakeholders, owning the full lifecycle from discovery and system design through production deployment, audit readiness, and organizational adoption. My focus is on systems that are reliable, explainable, and safe to operate in regulated environments.

My work focuses on building AI systems that organizations can safely rely on—not prototypes, demos, or prompt wrappers.

A core design principle across my work is anticipating failure modes—model drift, retrieval degradation, governance gaps, and operational misuse—and designing systems that surface issues early rather than hide them.

Background

I've spent my career at the intersection of enterprise technology and healthcare compliance. From migrating ML pipelines through FDA audits at Abbott, to building the first MCP-RAG knowledge platform for pharmaceutical content at Pfizer, I've learned that the hardest part of AI isn't the algorithms—it's shipping systems that work in production under real constraints.

Zero HIPAA violations across $51M+ in regulated AI portfolios. Zero FDA audit findings on ML system migrations.

Based in New York, I work with healthcare organizations that need someone who understands both the technical depth of modern AI systems and the regulatory reality of deploying them in clinical and commercial environments.

Representative Toolchain

ArchitectureCloud-native, service-oriented AI platforms
AI SystemsRetrieval-Augmented Generation (RAG), enterprise search
GovernanceAudit logging, policy enforcement, HITL workflows
MLOpsCI/CD, evaluation pipelines, monitoring and alerting
DataVector databases, structured metadata stores
LanguagesPython, TypeScript

Credentials

Google Cloud Professional Cloud Architect

Google Cloud Professional Cloud Architect

Google Cloud

Google Professional Machine Learning Engineer

Google Professional Machine Learning Engineer

Google Cloud

Microsoft Azure AI Engineer Associate

Microsoft Azure AI Engineer Associate

Microsoft

AWS Machine Learning Specialty

AWS Machine Learning Specialty

Amazon Web Services

Lean Six Sigma Black Belt

Lean Six Sigma Black Belt

IASSC

Stanford AI/ML Healthcare Specialization

Stanford AI/ML Healthcare Specialization

Stanford University

Clients

Pfizer

2024-2025

Abbott

2018-2020

IPG Health

2020-2024

Novartis

2020-2024

Sanofi

2020-2024

Medtronic

2017-2018

Eli Lilly

2014-2016

Amgen

2015-2017

Technical Expertise

AI/ML Systems

AI/ML Systems

  • RAG Systems
  • Graph-RAG
  • LLM Integration
  • NLP
  • Fine-tuning
  • Prompt Engineering
Platforms

Platforms

  • Azure ML
  • AWS SageMaker
  • AKS
  • MLflow
  • Kubeflow
  • Vertex AI
Data

Data

  • PostgreSQL
  • Neo4j
  • Pinecone
  • Qdrant
  • Delta Lake
  • Snowflake
Compliance

Compliance

  • HIPAA
  • FDA 21 CFR Part 11
  • MLR Compliance
  • SOC 2
  • Model Explainability
This portfolio reflects Principal-level ownership across problem definition, system architecture, production delivery, regulatory alignment, and long-term operational stewardship.

Search Keywords & Expertise

Forward Deployed Engineer portfolio • Principal AI Architect healthcare • Enterprise RAG architecture • AI governance platform • Regulated AI systems • Healthcare AI compliance • MLOps for regulated environments • AI auditability and traceability • Enterprise knowledge systems

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