Available for End-to-End AI Solution Architecture & GCP Implementations
I design and build reliable, high-performance systems where correctness, efficiency, and long-term maintainability matter. My work sits at the intersection of ML architecture, cloud infrastructure, and cost-aware performance engineering—supporting organizations moving from experimentation to large-scale AI deployment.
"The era of 'AI at any cost' is over. In 2026, leaders win by building AI systems that are high-performance, cost-governed, and energy-aware."
I help enterprises navigate the "AI Infrastructure Reckoning" by bridging the gap between high-level strategy and production-grade engineering. Whether starting from a blank slate or scaling a pilot, I deliver high-performance, cost-governed, and energy-aware systems.
| Engagement Model | Core Deliverables & Outcomes |
|---|---|
| A-to-Z Project Build | • Custom Generative AI Agents: Fully integrated, secure workflows using Gemini & Vertex AI Agent Builder. • Production ML Platforms: Scalable GKE/Vertex AI environments built for 99.9% reliability. |
| Consulting & Advisory | • Infrastructure Audit: Deep-dive into GPU/TPU utilization and cloud spend to identify 15–30% efficiency gains. • MLOps/LLMOps Strategy: Architecting end-to-end lifecycles (Pipelines, Model Garden, Feature Store). |
| Data Infrastructure Transformation | • Legacy to Modern AI Pipelines: Modernizing mission-critical workloads (IBM MQ/ACE) by migrating to high-throughput Modern OSS stacks including Apache Kafka, Redis, and Airflow. |
- Reduced TCO: Transitioning to efficient Small Language Models (SLMs) and right-sizing model selection to cut inference costs by up to 40%.
- Infrastructure-Native Intelligence: Leveraging ESNODE telemetry for node-level power, thermal, and GPU utilization optimization.
- Sustainability: Aligning AI deployments with Carbon-Aware Scheduling and enterprise cloud spend (FinOps) governance.
Founder & Managing Director
At ESNODE, I lead the development of a vendor-neutral AI infrastructure optimization platform. ESNODE bridges the gap between compute demand and energy realities by delivering real-time telemetry for modern AI clusters.
- GPU/CPU/Power Telemetry: Deep visibility into node-level power draw and thermal behavior.
- Inference Economics: Transitioning to efficient SLMs to maximize tokens-per-watt.
- Modernized Power Footprint: Scaling AI systems from on-prem servers to cloud-scale deployments responsibly.
| Category | Technologies |
|---|---|
| Core Languages |      |
| AI & Data Capabilities |      |
| AI & ML Libraries |     |
| AI & Data Platforms |    |
| Data & Messaging (OSS) |    |
| Vector Databases (OSS) |    |
| Workflow & Orchestration |   |
| Cloud & Infrastructure |   |
| Observability |   |
| Legacy & Enterprise Systems |      |
With over 25 years of experience in mission-critical environments (Banking, Defence, Telecommunications, Power Generation), my work is guided by:
- Robustness over novelty: Engineering for quiet reliability and long-term trust.
- Infrastructure-aware ML: Scaling architectures that respect hardware limits.
- Predictive Performance: Understanding why systems fail to build better operational correctness.
I am open to conducting confidential case studies for enterprises facing memory-related performance bottlenecks or GPU utilization challenges under full NDA.
📍 Based in Stockholm, Sweden / India
Designed for Reliability & Performance