Senior AI & Backend Engineer
Building intelligent systems
that scale.
I design and implement machine learning infrastructure and backend systems that power products used by millions.
About
I craft the invisible infrastructure that makes modern AI possible.
With over eight years of experience in backend development and machine learning systems, I specialize in bridging the gap between research and production. My work focuses on building robust, scalable systems that bring AI capabilities to real-world applications.
I believe in simplicity and clarity—both in code and in communication. The best systems are those that do complex things elegantly, with clean interfaces and well-defined boundaries.
Previously at companies like Meta and several ML-focused startups, I've led teams building everything from real-time inference pipelines to distributed training infrastructure serving billions of requests.
Backend Engineering
Systems built to last.
I architect backend systems with a focus on reliability, performance, and maintainability. Every design decision considers the long-term health of the codebase.
Distributed Systems
Designing fault-tolerant architectures that handle millions of concurrent connections with sub-millisecond latency.
API Design
Building RESTful and GraphQL APIs with thoughtful schemas, versioning strategies, and comprehensive documentation.
Database Architecture
Optimizing PostgreSQL, Redis, and time-series databases for high-throughput analytical and transactional workloads.
Infrastructure as Code
Implementing reproducible cloud infrastructure using Terraform, Kubernetes, and modern GitOps practices.
Machine Learning
From research to production.
I specialize in translating machine learning research into production-ready systems, ensuring models perform reliably at scale with proper observability.
MLOps & Infrastructure
Building end-to-end ML pipelines from data ingestion to model deployment, with comprehensive monitoring and versioning.
Large Language Models
Fine-tuning and deploying LLMs with efficient inference optimization, RAG architectures, and prompt engineering.
Real-time Inference
Designing low-latency serving systems that scale horizontally while maintaining consistent prediction quality.
Feature Engineering
Creating robust feature stores and pipelines that transform raw data into high-signal inputs for model training.
Contact
Say hello
I'm always interested in hearing about new projects, interesting challenges, or opportunities to collaborate on something meaningful.