Evaluation before rollout
I define quality checks up front (task accuracy, failure patterns, and acceptance thresholds) so AI features are shipped against evidence, not demos.

Software + AI engineering
Cloud-native platforms, APIs, and real-time systems shipped to production for international teams.
TypeScript, Node, NestJS, Angular, GCP, Firebase · pragmatic LLM integration and AI-assisted engineering where it earns ROI.
6+
Years delivering
Enterprise + startup
Contexts
Worldwide
Remote (EU, Americas, MENA, APAC)
Based in Tunisia (GMT+1). Remote-first for global teams — comfortable with overlap across Europe, the UK, MENA, North America (US & Canada), and Asia-Pacific (e.g. Australia) and similar zones.
Senior Software Engineer and AI consultant with 6+ years shipping cloud-native apps, scalable backends, real-time systems, and full-stack products. I optimize for maintainable architecture, measurable performance, and delivery that matches what stakeholders actually need.
Day-to-day AI engineering: Cursor-style assisted development, multi-model LLM workflows per task, and agent-style support for exploration and review—without lowering the production bar.
Systems and software engineering first; AI where it creates clear product value.
Production-grade backends, APIs, real-time systems, and cloud-native architectures on GCP and related stacks. Reliability, performance, and cost-aware operations for products that must hold up in the real world.
LLM integration, intelligent workflows, and automation inside real products—not slide decks. Pragmatic patterns for prompts, evaluation, and guardrails, aligned with user trust and business constraints.
Technical leadership for squads and consulting engagements: architecture decisions, stakeholder alignment, and predictable delivery across enterprise and startup contexts.
Practical rules I follow to ship AI features that are measurable, reliable, and safe under real usage.
I define quality checks up front (task accuracy, failure patterns, and acceptance thresholds) so AI features are shipped against evidence, not demos.
Every AI flow has constraints (input/output checks, policy boundaries, and fallback behavior) to keep user experience stable when models drift or fail.
Prompt versions, model choices, and key outcomes are tracked to make regressions visible and speed up incident debugging.
I tune model usage, caching, and orchestration patterns for predictable response time and cloud cost under real traffic.
For sensitive decisions, AI assists but does not finalize alone. Human-in-the-loop review is explicit in the workflow.
I ship in stages (pilot, monitor, expand) with rollback plans, so AI capabilities improve safely in production.
Representative technical work from client and product initiatives; details summarized or anonymized where appropriate (same selected projects as on my CV).
Named roles and contexts—AI-led delivery first, plus platform and enterprise depth. Tags match the themes in Selected work & outcomes.
More detail on request.
ZUPdeCO
Senior consultant · AI & product technical strategy
2025 – present · France, remote
Contract engagement leading design and delivery of products where speed, architecture, and AI-enabled workflows must coexist.
Tested4you Business
Senior consultant · AI & cloud-native apps
2024 – present · France, remote
Building and evolving applications where real-time behavior and cloud-native patterns matter under changing requirements.
Carrefour Links (via Devoteam)
Senior consultant · full-stack & cloud
2023 · France, remote
Production features and platform work on a cloud-based retail stack with tight reliability expectations.
PwC UK (via Play Studios LTD)
Senior consultant · backend & solutions
2023 · UK, remote
High-performance backend work and structured technical communication for enterprise stakeholders.
Same person—filters only change emphasis. Tags highlight themes across one track record.
Featured — AI & modern delivery
In recent engagements, combined rigorous software delivery with modern AI practice: AI-assisted development (including Cursor-style workflows), multi-model LLM usage matched to each task, and agent-style automation for exploration, review, and documentation—without compromising architecture or production standards. Built and integrated AI-driven capabilities including automation, intelligent workflows, and data processing alongside classic full-stack and cloud work.
Led the development of a comprehensive full-stack application using Angular and Node.js with NestJS, implementing real-time communication via Socket.IO. This project improved user engagement by 40% and reduced data latency by 60%.
Designed and implemented scalable cloud-based solutions using Google Cloud Platform (GCP), resulting in a 30% reduction in infrastructure costs and a 50% improvement in application performance.
Spearheaded the transition to a microservices architecture using Docker and Kubernetes, improving deployment frequency by 200% and reducing mean time to recovery by 60%.
Led a team of 7 developers in an agile environment, successfully delivering 5 major projects on time and within budget. Implemented code review processes that improved code quality and reduced bugs by 40%.
Short write-ups on delivery, cloud, real-time systems, and AI engineering practice.
Logos for recognition; scope and role varied by engagement.
Core stack — what I reach for most weeks. Details by area below.
Browse tools by area (compact view):
How I ship today: assisted development, model choice per task, and disciplined integration of LLMs into real products.
Open to senior engineering, technical lead, and consulting conversations. Send a short brief (role, stack, timeline) and I will reply with fit and availability.
Site URL for signatures:yassine-elouni-profile.web.app