Backend · AI Engineering
I design and build production-grade backend systems and data pipelines, combining Java (Spring Boot), Python, and distributed architectures to deliver scalable APIs, event-driven systems, and AI/ML-powered features. My work spans microservices, streaming pipelines, and machine learning systems — from data ingestion and model training to deployment and monitoring in real-world environments.
Backend Engineering
Java · Spring Boot · REST APIs · Microservices · OAuth2/JWT
Data & Distributed Systems
Kafka · PostgreSQL · MongoDB · Event-driven architecture · Streaming
AI & Machine Learning
Python · PyTorch · ML pipelines · LLM integration · Model deployment
1) Align
System design, interfaces, and success criteria before implementation.
2) Build
Incremental delivery with tests, monitoring, and production readiness.
3) Core technologies
Documentation, observability, and reliable handoff for production systems.
Core profile
Backend · Data · ML Systems
Backend services, data pipelines, and AI-powered applications in production.
Programming languages
Java · Python · TypeScript · SQL
Strong backend foundations with practical experience across modern web and data systems.
Handoff
Spring Boot · Angular · PostgreSQL · Kafka · PyTorch
Built and deployed across APIs, microservices, distributed workflows, and applied machine learning.
Services
Backend-first engineering with strong foundations in APIs, distributed systems, data engineering, and applied machine learning.
Backend Systems & APIs
Production-grade backend services with strong API design, authentication, asynchronous workflows, and reliability patterns such as retries, idempotency, and fault-tolerant processing.
Data Engineering & Distributed Workflows
Data ingestion, preprocessing, validation, and event-driven processing across SQL/NoSQL systems, streaming backbones, and observable pipelines.
AI / ML Engineering
End-to-end ML engineering including data preparation, model training, evaluation, deployment, and integration into backend systems. Covers both predictive models and LLM-powered workflows.
Projects
A selection of backend systems, data platforms, and AI/ML projects that demonstrate production engineering, system design, and applied machine learning.
Consultations platform with routing, notifications, and payments. Built for reliable booking flows, clear failure handling, and observable services.
AI-assisted app generation with secure auth and payments. Integrated LLM capabilities with Spring AI and hardened the platform for production use.
Adaptive learning platform with dynamic scheduling, instructor ranking, and integrations. Focused on dependable day-to-day operations.
Edge ML with peer-to-peer coordination and evaluation loops. Covers data ingestion, training, inference, and monitoring on constrained devices.
Dataset and experimentation pipeline for building analytics. Deterministic preprocessing, quality checks, visualization, and neural baselines to compare models fairly.
Engineering approach
I focus on clarity, reliability, and maintainability: understand the problem, design the system, ship incrementally, and keep production quality visible.
Clarify constraints, interfaces, acceptance tests, and success metrics.
Break scope into weekly thin slices with demos and instrumentation.
Ship the slices with tests, tracing/metrics, and rollback plans.
Document, train, and instrument ownership so your team can run it.
I’m currently looking for backend or AI engineering roles where I can contribute to building scalable systems, data pipelines, and production-ready machine learning applications. If you’re working on challenging problems in distributed systems, data platforms, or AI — I’d be glad to connect.