Production AI is much more than sending a prompt and displaying the response
production systems need to handle everything that happens around that LLM
Cloud & AI Solutions Architect
Building secure, scalable, and business-ready cloud and AI platforms on AWS.
I help startups and enterprises architect, migrate, and optimize AWS workloads and take AI systems from prototype to production, with a strong focus on reliability, performance, security, and cost efficiency.
As a Cloud & AI Solutions Architect, I design landing zones, networking foundations, serverless systems, container platforms, CI/CD pipelines, and observability stacks — alongside generative AI applications, retrieval-augmented generation (RAG) pipelines, AI agents, and model operations that teams can govern, operate, and scale with confidence.
Reference architectures for secure, scalable, and resilient systems aligned with the AWS Well-Architected Framework, including the Generative AI Lens.
Production-grade AI applications on Amazon Bedrock and SageMaker: RAG over private knowledge, prompt and evaluation pipelines, guardrails, and cost-aware model selection.
Tool-using agents and event-driven workflows that automate document processing, support operations, and internal back-office tasks with human-in-the-loop controls.
Ingestion, vector stores, feature and embedding pipelines, model deployment, monitoring, and retraining workflows that keep AI systems accurate over time.
Phased migration strategies, re-platforming, and modernization of legacy workloads to cloud-native and AI-ready AWS services.
CI/CD automation, observability, operational runbooks, and incident-ready systems for production cloud and AI workloads.
EC2, ECS, EKS, Lambda, Step Functions, API Gateway, S3, CloudFront, RDS, DynamoDB, EventBridge, SQS, Route 53, IAM.
Amazon Bedrock, SageMaker, Bedrock Knowledge Bases and Guardrails, OpenSearch vector search, Textract, Comprehend, Transcribe, Rekognition.
RAG pipelines, embeddings and vector databases, agent and tool-calling frameworks, prompt engineering, evaluation harnesses, and LLM observability.
Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, CloudWatch, and production observability workflows.
Glue, Athena, Kinesis, OpenSearch, PostgreSQL with pgvector, Redis, and streaming or batch data processing pipelines.
Laravel, React, Node.js, Python, MySQL, and integration-ready API architectures for AI-enabled products.
Delivered staged AWS migrations with rollback-safe releases, cutover planning, and minimal downtime for business-critical systems.
Turned AI proof-of-concepts into governed, monitored services with grounded retrieval, evaluation checkpoints, and clear guardrails before release.
Implemented CI/CD and infrastructure automation that reduced manual deployment effort and improved release confidence.
Improved cloud and inference cost visibility through right-sizing, managed services, model selection, caching, and usage-aware design decisions.
production systems need to handle everything that happens around that LLM
When building an AI application, it's tempting to tightly integrate everything with one LLM provider.It works initially.But as the application grows, that decis...
In a growing engineering team, multiple developers, DevOps engineers, platform engineers, and automation pipelines are continuously provisioning AWS resources.
As AWS environments grow, one of the first operational challenges organizations face is resource management.
Type "gift for my wife" into a traditional e-commerce search box built on BM25/TF-IDF keyword matching, and you'll get exactly what you asked for: nothing useful
You make the decision to upgrade your EBS volume through the AWS Console, eagerly wait for the "volume modification" to complete, and then SSH into your instance... only to run df -h and see the same old storage size staring back at you.
Free Email Verifier tool. Upload a list, validate syntax, MX records, and risky addresses, then download cleaned results.
Credentials: 12+ years in software engineering with hands-on AWS architecture, migration, production operations, and applied AI delivery experience.
Client domains: healthcare, SaaS, non-profit operations, data platforms, and internal enterprise systems.