Senior Systems Architect & AI Infrastructure Engineer
Rust · Go · Performance Engineering · Distributed & Mission-Critical Systems
I design and build reliable, high-performance systems where correctness, efficiency, and long-term maintainability matter.
My work is grounded in deep systems engineering, with a growing focus on AI infrastructure, agent runtimes, and cost-efficient inference.
With 25+ years in enterprise and industrial environments, I am motivated by hard problems—especially those involving memory behavior, system limits, and performance under real-world constraints.
- Rust-first AI infrastructure for safe, predictable, and efficient systems
- LLM & agent runtimes with attention to memory locality, scheduling, and cost
- Performance & cost optimization research across AI and enterprise platforms
- Bridging legacy systems and modern AI workloads responsibly
I am particularly interested in AI systems that must run reliably at scale, not demos.
- Memory management challenges in Python, C++, and Java
- Safe, high-performance alternatives using Rust and Go
- CPU-efficient inference and non-GPU-centric AI execution
- Agent orchestration, data pipelines, and decision systems
- Observability, failure modes, and operational correctness
If an enterprise or organization is facing memory-related performance bottlenecks on private platforms, I am open to conducting a confidential case study under full NDA and privacy guarantees—purely out of professional curiosity and a desire to learn, improve, and innovate.
- Rust (systems safety, performance, WASM)
- Go (distributed systems, services, tooling)
- Python (analysis, AI workflows, orchestration)
- Java & C++ (legacy systems, performance diagnosis)
- LLMs · AI Agents · LangGraph-style orchestration
- Vector databases · PostgreSQL · pgvector
- ETL · Airflow · Analytics pipelines
- CPU-first and cost-aware inference strategies
- Distributed systems · Event-driven architectures
- Linux internals · RHEL · Oracle Solaris
- Legacy & HPC environments
- Mainframe exposure (IBM Z ecosystems)
- IBM MQ · IIB / ACE · DataPower
- SOA · Messaging · Enterprise integration
- Mission-critical reliability engineering
TypeScript and frontend technologies are not my primary focus.
My background is rooted in systems, middleware, data integration, and analytics engineering.
| Area | Description |
|---|---|
| AI Decision Systems | Industry benchmarking, anomaly detection, predictive analytics |
| Agent Runtimes | Multi-agent coordination with performance and cost awareness |
| ESNODE Runtime (Concept) | CPU-first, hardware-agnostic LLM inference |
| Enterprise Optimization | Memory & throughput analysis for large platforms |
I have worked across environments where failure is not an option:
- Banking & Finance
- Manufacturing & Industrial Systems
- Airlines
- Defence & Government (Military Systems)
- Telecommunications
- Power Generation & Utilities
These domains shaped my bias toward robustness, observability, and correctness over novelty.
I enjoy:
- Mission-critical systems
- Deep problem-solving
- Understanding why systems fail—not just how to fix them
- Building software that engineers can trust years later
I value quiet reliability over hype, and engineering discipline over trends.
📍 Based in Sweden / India 🔗 LinkedIn: https://www.linkedin.com/in/khajamoddin/ · 📧 Email : [email protected]