Why RAG Needs End-to-End Evaluation?
A production RAG system is not one component. It is a multi-stage retrieval and generation pipeline, and every stage can introduce failure.
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.
What I Do
AWS Architecture Design
Reference architectures for secure, scalable, and resilient systems aligned with the AWS Well-Architected Framework, including the Generative AI Lens.
Generative AI & LLM Solutions
Production-grade AI applications on Amazon Bedrock and SageMaker: RAG over private knowledge, prompt and evaluation pipelines, guardrails, and cost-aware model selection.
AI Agents & Intelligent Automation
Tool-using agents and event-driven workflows that automate document processing, support operations, and internal back-office tasks with human-in-the-loop controls.
Data Platforms & MLOps
Ingestion, vector stores, feature and embedding pipelines, model deployment, monitoring, and retraining workflows that keep AI systems accurate over time.
Cloud Migration & Modernization
Phased migration strategies, re-platforming, and modernization of legacy workloads to cloud-native and AI-ready AWS services.
DevOps & Platform Reliability
CI/CD automation, observability, operational runbooks, and incident-ready systems for production cloud and AI workloads.
Technologies
AWS Core Services
EC2, ECS, EKS, Lambda, Step Functions, API Gateway, S3, CloudFront, RDS, DynamoDB, EventBridge, SQS, Route 53, IAM.
AWS AI & ML Services
Amazon Bedrock, SageMaker, Bedrock Knowledge Bases and Guardrails, OpenSearch vector search, Textract, Comprehend, Transcribe, Rekognition.
AI Engineering
RAG pipelines, embeddings and vector databases, agent and tool-calling frameworks, prompt engineering, evaluation harnesses, and LLM observability.
Platform & DevOps
Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, CloudWatch, and production observability workflows.
Data & Analytics
Glue, Athena, Kinesis, OpenSearch, PostgreSQL with pgvector, Redis, and streaming or batch data processing pipelines.
Application Engineering
Laravel, React, Node.js, Python, MySQL, and integration-ready API architectures for AI-enabled products.
Proof & Outcomes
Reliable Cloud Migrations
Delivered staged AWS migrations with rollback-safe releases, cutover planning, and minimal downtime for business-critical systems.
AI Moved to Production
Turned AI proof-of-concepts into governed, monitored services with grounded retrieval, evaluation checkpoints, and clear guardrails before release.
Faster Delivery Pipelines
Implemented CI/CD and infrastructure automation that reduced manual deployment effort and improved release confidence.
Operational Cost Discipline
Improved cloud and inference cost visibility through right-sizing, managed services, model selection, caching, and usage-aware design decisions.
Recent Writing
Read cloud & AI architecture insights →
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Free Tools
Email Verifier
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.