Thirteen years across cloud, data, and enterprise systems — now focused on agentic enablement at production grade. I design the governed, auditable paths that let teams wire LLMs and agents into backend systems: MCP server frameworks, tool-use SDKs, and the middleware that absorbs identity, data governance, and guardrails.
Defined an MCP → federated-identity → enterprise-systems topology, now the org-standard pattern for agentic workloads. First proven across SAP HANA, OData, and SaaS landscapes — cutting new integration build time from weeks to days.
Shipped an MCP framework with shared SDK libraries that absorb federated auth, query governance, and security guardrails — so consuming teams inherit production-grade safety by default instead of rebuilding it per system. Pioneered the FastMCP runtime in production.
Architecting AIOps across three agentic domains — Monitoring & Alerting (triage, Dataproc/BigQuery monitoring, vulnerability tracking), Operations (change coordination, patch-day automation, system-health analysis), and Knowledge & Query (L1 knowledge and troubleshooting agents).
Productionized an agent giving SRE and platform teams natural-language access to system health, sessions, and workload analytics on the HANA platform — now the reference implementation other agents are built from.
Drove FinOps governance delivering verified, recurring cost savings — BigQuery slot reservations, GCP committed-use discounts, rightsizing, and quota management — with no performance degradation.
Built Terraform provisioning frameworks that eliminated ~80% of manual ClickOps and self-service platforms that offloaded ~70% of L1/L2 tasks — plus CLI-generated, production-ready MCP servers and an interactive docs site that turns one-off builds into a repeatable authoring path.
Architected Apache Spark / Dataproc platforms processing 200M+ events per day — PySpark pipelines over Pub/Sub streaming and batch ingestion at multi-terabyte scale, with GKE-based orchestration and capacity monitoring.
The platform layer for agentic workloads — governed, auditable, and reusable across teams.
Multi-cloud platforms with a deep Google Cloud foundation, built for resilience and cost.
Big-data platforms supporting streaming and batch ingestion at multi-terabyte scale.
Observability, chaos engineering, and InfoSec collaboration for audit-ready systems.
Leading AI & agentic platform engineering (MCP) and AIOps — plus large-scale GCP migrations, Spark/Dataproc data platforms, FinOps governance, and SRE.
Ansible-based automation for patch management; SAP/Linux hardening for audit mandates; HA/DR with Pacemaker and SAP HANA clusters for Fortune 500 clients.
System automation and DR setups for global SAP clients; contributed to the HANA Center of Excellence on performance tuning and migration assessments.
I write about agentic platforms, MCP, AIOps, and what it actually takes to run AI in an enterprise. Live posts below — tap through to read or comment on LinkedIn.
Why brittle RPA is giving way to agentic AI — agents that read documents by meaning, call ERP and procurement tools through controlled interfaces, and escalate only true exceptions. Scripts for stable tasks, agents for variable workflows, humans for judgment boundaries.
Read on LinkedIn ↗ inLinkedIn · PostA defense of the Model Context Protocol: standardized auth, auditing hooks, and guardrails for tool access where no clean API exists. Context bloat isn't MCP's fault — it's architecture. Atomic, focused tools stay surgical.
Read on LinkedIn ↗I write and build around agentic platforms, MCP, and AIOps. Always happy to compare notes or talk shop — email is the fastest way to reach me.
suryal.k90@gmail.com