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Nidal Raed

@nidalraed

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Senior AI Full Stack Engineer turning medical ML and high-throughput backends into reliable, production systems.

Finland

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What I'm looking for

I’m looking for a team where I can own end-to-end AI product delivery—optimizing inference, designing scalable APIs, and shipping HIPAA-grade, measurable performance improvements in production.

I’m a Senior AI Full Stack Engineer with 10 years of progressive experience spanning backend systems, distributed architectures, and applied machine learning. I specialize in bridging ML model development with high-throughput engineering so teams can ship measurable performance and reliability gains.

Most recently, I designed and shipped a production-grade retinal screening platform integrating PyTorch inference pipelines with HIPAA-compliant data infrastructure for clinical workflows at scale. I reduced diagnostic processing latency by 42%, maintained clinical-grade AUC thresholds (>=0.97), and cut physician annotation time by 35% by building a multimodal LLM pipeline for note summarization and anomaly flagging.

I’m comfortable owning systems end-to-end—from GPU inference optimization and vector search design to React frontends and CI/CD rollout strategy. Earlier, I built distributed telemetry ingestion at 500K+ events/second with 99.99% uptime using Kafka and Spring Boot, and I led the decomposition of a monolith into domain-scoped microservices to isolate fault boundaries and enable independent scaling.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Full Stack Engineer

Current

Optomed Plc

Aug 2023 - Feb 2026 (2 years 6 months)

Architected a production retinal screening platform integrating FastAPI microservices with PyTorch inference, reducing diagnostic processing latency by 42% while maintaining clinical-grade AUC (>=0.97). Built an LLM clinical note pipeline (GPT-4-vision + FAISS) cutting physician annotation time by 35%, and delivered sub-200ms P99 GraphQL queries at 1.5M+ records with HIPAA-aligned encrypted, audit

fastAPI PyTorch GPT 4 GraphQL PostgreSQL Redis Kubernetes ResNet50

Senior Full Stack Engineer

Nokia

Sep 2020 - Jun 2023 (2 years 9 months)

Built distributed telemetry ingestion services in Java Spring Boot with Apache Kafka, sustaining 500K+ events/second at 99.99% uptime. Developed streaming fault-detection algorithms reducing incident detection latency by 30% and suppressing 60%+ false positives, and delivered React network analytics dashboards that reduced time-to-identify faults by 25%.

Java Spring Boot Apache Kafka GraphQL React D3.Js Microservices Architecture

Full Stack Engineer

Nokia

Dec 2018 - Nov 2019 (11 months)

Developed Node.js/Express backend services for network configuration management, improving API response times by 28% via index tuning and N+1 query elimination. Built React/Redux provisioning tools and JWT-based RBAC, reducing manual configuration effort by 20% and strengthening access control, and integrated the ELK stack to cut incident time-to-resolution by surfacing actionable diagnostics.

Node MongoDB PostgreSQL React Redux Elasticsearch Logstash

Backend Engineer

City of Vantaa

Jun 2016 - Jan 2019 (2 years 7 months)

Built Java backend services for municipal citizen data platforms, improving throughput by 35% under peak load through schema/index optimization and load-balanced deployment. Implemented asynchronous batch processing with ActiveMQ, reducing end-user-visible delays by 40%, and delivered REST integrations with idempotency, retries, and versioning while improving observability with correlation IDs and

Java MySQL ActiveMQ Nginx Connection Pooling Correlation IDs