Skip to main content

Senior Full Stack & AI Engineer

Eric Kouassi

Engineering since 2011, applied AI since 2019. GraphRAG platforms, multi-agent orchestration, and computer vision pipelines, with the distributed backends and evaluation systems underneath them.

I started in frontend engineering in 2011 and worked down the stack, through backend services, data pipelines, and computer vision, into applied LLM systems. That range is the point. Designing a retrieval platform means making decisions about graph modelling, service architecture, evaluation, and interface design at the same time, and I have shipped every one of those layers in production.

Areas of focus

AI Systems
Retrieval and reasoning architectures taken to production.
Agentic AI
Tool-using systems with bounded, observable control flow.
Evaluation & Safety
Making model output measurable, so improving it is an engineering task and not an argument.
Backend & Systems
Service design and the distributed plumbing underneath it.
Data Engineering
Moving, storing, and retrieving data at working scale.
Computer Vision
Inference pipelines where latency and false negatives both cost.
Cloud & Infrastructure
Getting systems deployed, observable, and recoverable.
Frontend
Interfaces that make complex systems usable.

How I work

  1. Architecture before implementation

    The expensive decisions are made before the first line of code. Getting boundaries right early is what keeps a system changeable two years later.

  2. AI output has to be measurable

    A model response that cannot be evaluated cannot be improved or trusted. Evaluation belongs in the deployment path, not in a notebook after the fact.

  3. Reliability matters as much as intelligence

    Users do not experience model quality in isolation. They experience latency, failure modes, and recovery, so those are product decisions, not operational afterthoughts.

  4. Simple systems beat clever ones

    The cheapest architecture to operate is usually the one with the fewest moving parts. Complexity should be paid for by a requirement, not by preference.

Education

  1. 2017 to 2018

    Postgraduate Master programs

    University of Parma

  2. 2008 to 2011

    Bachelor's Degree, Computer Science

    University of Parma

Location
Parma, Italy
Availability
Open to new opportunities