Grounded AI workflows
Designing retrieval-augmented generation, source grounding, evaluation patterns, and human-review controls for high-trust enterprise use cases.
Enterprise AI & Cloud Engineering
Principal AI/ML
Platform Engineer.
I design and engineer governed GenAI, MLOps, and cloud data platforms that help regulated organizations turn AI ideas into reliable, secure, and usable enterprise capabilities.
18+ yearsEnterprise technology delivery
5x AWSCloud and data certifications
CKADKubernetes application development
AI/MLPlatform, RAG, and workflow focus
Capabilities
The platform thinking behind dependable AI.
The model is only one part of an enterprise AI solution. My focus is the engineering around it: data quality, secure APIs, deployment, observability, evaluation, and human review.
Operationalizing machine learning
Turning notebook-based models into secure, observable services with FastAPI, contracts, confidence handling, logging, versioning, and workflow integration.
AI-ready data foundations
Building governed ingestion, transformation, and lakehouse patterns that make reliable data available for analytics, ML, and GenAI applications.
Generative AI & RAG
AI/ML Platform Engineering
MLOps & LLMOps
Amazon Bedrock
Python & FastAPI
AWS Glue & PySpark
Apache Iceberg
OpenSearch Vector Search
Docker & Kubernetes
Terraform & CI/CD
API & Event-Driven Design
IAM, KMS & Secrets Management
Observability & Auditability
Financial & Insurance Workflows
Delivery focus
Enterprise use cases, explained without the hype.
My work combines architecture ownership with hands-on engineering across cloud data, AI/ML application enablement, and modern workflow integration.
Enterprise AI analysis workflows
Designing RAG-enabled patterns for document analysis, case summarization, evidence retrieval, traceability, and human review in control-oriented environments.
Provider payment & overpayment analytics
Building batch and incremental pipelines, data-quality controls, curated lakehouse datasets, and secure services that support payment investigation workflows.
Claims classification & reinsurance support
Operationalizing TF-IDF-based text classification through an inference API, confidence-aware routing, validation workflows, and enterprise operational controls.
Secure delivery by design
Applying Terraform, CI/CD, containerization, IAM, secrets management, logging, and observability patterns to make technical solutions supportable at scale.
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 platform work.
AWS ML Engineer – Associate
AWS Data Engineer – Associate
AWS Solutions Architect – Associate
AWS Developer – Associate
AWS SysOps Administrator – Associate
Certified Kubernetes Application Developer
Python Certified Associate Programmer
PySpark Certification
DeepLearning.AI & Stanford Online
Machine Learning Specialization
View certificate ↗
Structured learning in supervised learning, model evaluation, and applied machine-learning workflows.
DeepLearning.AI
Deep Learning Specialization
View certificate ↗
Advanced coursework covering neural networks, deep learning foundations, and practical model-development concepts.
Connect
Let’s discuss the next AI platform challenge.
For Principal AI/ML Platform Engineering, GenAI, MLOps, and cloud data platform opportunities, use the contact button or connect through LinkedIn.