Designing production GenAI architectures, autonomous agents, and distributed ML platforms. Bridging last-mile intelligence with the reliable engineering required to sustain enterprise operations.
Mathematical modeling is only the last mile of enterprise AI. A high-performing model has zero business impact without quality data, if it suffers from training-serving skew, lacks data-to-model lineage, or runs without aligned evaluation gates.
True governance and business leverage comes from quality engineering of the full operational stack: scalable feature stores, artifact versioning, runtime and infrastructure observability, rigorous offline and online evaluation, and decoupled architectures.
Today, as a GenAI and ML Solutions Architect, my focus is designing the cloud software and systems that operationalize intelligence: autonomous multi-tool agents via AWS Bedrock AgentCore, reproducible training pipelines, and cost-optimized AWS CDK infrastructure built to withstand production scale.
Focus: Architecting end-to-end cloud and AI solutions that bridge mathematical depth to business reality.
Autonomous cloud economics agent built with AWS CDK, Bedrock AgentCore Gateway, and Next.js on Amplify to optimize commitment ROI.
Multi-stack AWS CDK platform isolating Feast (feature store) and MLflow (champion registry) within private VPC subnets with Fargate compute.
Domain-adapted SLM pipeline fine-tuning Llama-3.2-3B on AWS SageMaker with IBM Docling document parsing and low-latency Qdrant serving.
Scaling an ML platform from a single model to a multi-model catalog requires a relational database to manage state and an API boundary to enforce communication. Here is how I built the CQRS API and network isolation layer to scale from scripts to systems.
Data scientists solve the last-mile problem. ML engineers build the road. Here is what the ML platform engineer's piece actually looks like when built from scratch, grounded in 27 architectural decisions and hard-earned production lessons.
Whether you are a hiring manager recruiting senior ML platform and AI engineering talent, or an existing business exploring platform solutions and architectural discovery, I would love to connect. I engage directly on full-time roles, contract projects, and complex engineering challenges.
Tell me about your team, your open role, or the systems you are looking to build.
