Available for Cloud & AI Architecture Consulting

Cloud & AI Solutions Architect

Designing resilient AWS cloud platforms and taking generative AI systems from prototype to production.

I help startups and enterprises architect, migrate, and optimize mission-critical AWS workloads — and build production-ready generative AI, RAG, and agentic systems with a rigorous focus on reliability, security, and cost efficiency.

12+ years software & cloud engineering Based in Chennai, India Consulting Worldwide

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, serverless, and AI-ready AWS services.

DevOps & Platform Reliability

CI/CD automation, infrastructure as code, observability, operational runbooks, and incident-ready systems for production cloud and AI workloads.

AWS Core & Compute

Foundational infrastructure, compute, and networking for production services.

ECS EKS Lambda Step Functions API Gateway S3 CloudFront VPC

Applied AI & Bedrock

Foundational and fine-tuned model infrastructure on managed AWS AI stacks.

Amazon Bedrock SageMaker Bedrock Knowledge Bases Guardrails RAG Pipelines Claude / Llama

Databases & Vector Stores

High-throughput transactional, analytical, and semantic search data stores.

Aurora RDS DynamoDB OpenSearch pgvector Redis Athena

Platform & Infrastructure as Code

Declarative automation, delivery pipelines, and deep observability.

Terraform Docker Kubernetes GitHub Actions CloudWatch OpenTelemetry

Reliable Cloud Migrations

Delivered staged AWS migrations with rollback-safe releases, cutover planning, and zero unplanned downtime for mission-critical systems.

AI Moved to Production

Turned fragile AI prototypes into governed, monitored services with grounded retrieval, latency optimization, and automated evaluation checkpoints.

Accelerated Delivery Pipelines

Implemented CI/CD and immutable infrastructure automation that eliminated manual deployment toil and increased release velocity.

Cloud & Inference Cost Discipline

Reduced compute and LLM token expenditures through architectural right-sizing, caching, model tiered routing, and usage-aware scaling.

Industry Experience: Healthcare systems, B2B SaaS, data platforms, and high-compliance enterprise workloads.

Available for strategic architecture reviews, Well-Architected audits, and hands-on consulting.

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Read all articles

AWS Landing Zone - A Technical Guide to Enterprise Cloud Foundations

When an organization starts using AWS, the first few teams can usually create resources manually:

If You Can’t Measure Your LLM, You Can’t Reliably Improve It

Many AI teams monitor latency and token usage but still don't know why their production AI system is failing.

Stop Choosing Models by Benchmark Score

A model that ranks #1 on a public benchmark may be the wrong model for your production workload.

Vector Search Isn't Always Enough for RAG

Vector search is powerful because it retrieves documents based on semantic meaning, not just exact words.

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.

Production AI is much more than sending a prompt and displaying the response

production systems need to handle everything that happens around that LLM

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