Professional Summary
I’m an AI Platform and MLOps Engineer who has grown
from a strong DevOps and infrastructure foundation
into building production-ready AI and ML platforms.
My approach is bottom-up: infrastructure → cloud-native
platforms → MLOps → LLMOps → production AI systems. This
foundation helps me design AI platforms that are not only intelligent,
but also scalable, reliable, observable, secure, and operationally
efficient.
Currently, I work on enterprise AI platform
engineering, building and operating production-grade systems
for Generative AI, agentic workflows, RAG, ML pipelines, and
cloud-native infrastructure.
My experience includes designing enterprise AI platforms,
multi-agent architectures, Graph RAG systems, hybrid retrieval
pipelines, data workflows, Kubernetes platforms, distributed storage,
event-driven systems, and production observability.
Earlier in my career, I focused on DevOps, AWS
infrastructure, application deployment, databases, automation,
security, and cloud platform operations.
This progression from infrastructure engineering to AI platform
engineering gives me a practical understanding of the complete
lifecycle—from provisioning infrastructure and deploying workloads
to operating ML and GenAI systems in production.
I’m particularly interested in building the engineering foundations
that enable AI teams to move from experimentation to reliable
production systems, with an emphasis on platform engineering,
MLOps, LLMOps, automation, scalability, observability, and AI
infrastructure.
Core areas of expertise:
AI Platform Engineering | MLOps | LLMOps | GenAI | Agentic AI | RAG |
Graph RAG | Kubernetes | Cloud Infrastructure | ML Pipelines |
Distributed Systems | Platform Engineering | Observability | Automation
I enjoy solving complex infrastructure and platform challenges and
turning them into reliable, scalable systems that
enable teams to build and operate AI applications efficiently.