Solution Architect | AI/ML & GenAI Engineer | Cloud Architect | Systems & Semiconductor R&D

I have 20+ years of experience designing scalable enterprise and cloud-native systems across AWS, Azure, and GCP. My current focus is on building AI-native platforms, custom LLM/Transformer architectures, agentic workflows, cloud modernization, and applied AI for enterprise domains such as retail, manufacturing, automotive, and document intelligence.

I like working at the boundary of software architecture, AI research, systems engineering, and semiconductor innovation.

• Custom Transformer and LLM architecture experiments focused on lower memory movement, smaller parameter count, and efficient inference.
• Agentic AI and multi-agent workflow design for software engineering, architecture generation, document processing, and enterprise automation.
• LLM-powered extraction, mapping, and structured JSON generation for enterprise document automation.
• Multi-agent solution patterns using planner agents, researcher agents, coding agents, reviewer agents, retrieval agents, critic/evaluator agents, and workflow orchestration.
• Advanced Reinforcement Learning concepts for autonomous decision-making, optimization, planning, reward modeling, and adaptive AI systems.
• Custom Transformer and LLM architecture experiments focused on lower memory movement, smaller parameter count, and efficient inference.
• LLM-powered extraction, mapping, and structured JSON generation for enterprise document automation.
• Exploring OS, semiconductor, and accelerator ideas for future AI systems.
• Applied ML using PyTorch, Scikit-learn, Hugging Face, Ollama, AWS Bedrock, Azure OpenAI, and Vertex AI.
• Creating cloud-native architectures that are secure, scalable, and cost-aware

• Multi-cloud solution architecture across AWS, Azure, and GCP.
• Serverless, ECS/Fargate, Lambda, API gateways, event-driven systems, DynamoDB, Firestore, BigQuery, and cloud-native integrations.
• Retail loyalty, campaign, voucher, customer savings, and large-scale transactional data systems.

• Exploring custom operating-system architecture, per-core scheduling, capability-based security, syscall gates, memory protection, and multi-architecture boot flows.

• Exploring custom ISA, AI accelerators, RISC-V/Shakti ecosystem, simulation, verification, and PDK-backed chip IP.
• Research interest in efficient compute architectures for LLMs and AI workloads.

• ML-assisted inverse design for silicon-based block copolymer self-assembly and lithography-oriented materials discovery.

I am exploring alternatives and improvements to standard Transformer architectures with a focus on:

• Reducing parameter count without losing reasoning quality.
• Lowering data movement between memory and compute.
• Improving context handling using memory banks, gating, compression, and structured retrieval.
• Designing architectures that are practical for small GPUs and edge/low-resource environments.
• Studying mathematical alternatives to attention-heavy computation for LLMs, image generation, and video generation.

Research keywords:
Efficient Transformers · Memory-efficient Attention · GQA · KV Cache Optimization · Low-VRAM Training · AI Accelerators · Custom ISA · RISC-V

AI/ML & Agentic AI: PyTorch · Scikit-learn · Hugging Face · LangGraph · CrewAI · AutoGen · Ollama · AWS Bedrock · Azure OpenAI · Vertex AI Cloud: AWS · Azure · GCP · Lambda · ECS/Fargate · Cloud Run · BigQuery · Firestore · DynamoDB
Languages: Python · Java · C# · JavaScript · TypeScript · C/C++
Architecture: Microservices · Event-driven Systems · Serverless · API Management · Distributed Systems
DevOps: Docker · Kubernetes · Jenkins · GitHub Actions · Bitbucket · Terraform/CloudFormation
R&D: Operating Systems · RISC-V · Custom ISA · AI Accelerators · VLSI Exploration

• Building AI-native engineering workflows.
• Improving resume/document automation using LLM-based manifests and deterministic rendering.
• Designing efficient custom LLM/Transformer architectures.
• Exploring OS, semiconductor, and accelerator ideas for future AI systems.
• Creating cloud-native architectures that are secure, scalable, and cost-aware.

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"Architecting cross-domain solutions where AI, cloud, digital transformation, business value, and next-generation compute come together."