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The founder

Enzo Tripoli

Founder and technical lead - ET Technology Consulting

Python · Go · PostgreSQL · React · gRPC · applied AI & LLMs

Nine years designing, building and operating systems that run in production under real constraints: volume, cost, reliability, compliance. I started with large-scale data pipelines, moved on to distributed backends, and spent the last two years putting AI into production - the real kind, which has to answer correctly, quickly, and without blowing up the bill.

What interests me is always the same point: the distance between a raw data point and a decision. Shortening it means being able to read a PostgreSQL query plan in the morning and scope a business need in the afternoon. That skill is what the firm is built on, and I carry it on every engagement.

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A few markers

9 yrs
in data, backend and AI
80,000+
active users on a backend I designed
÷ 50
LLM inference cost, at constant quality
-30-40%
API latency after refactoring
10
developers led, internal and external
50
physical servers operated and automated

What I do

01

Make data flow

ETL pipelines over millions of rows, multi-source ingestion, performance-oriented modelling, advanced PostgreSQL (indexing, materialised views, query plans), BigQuery, Elasticsearch, Redis.

02

Make AI reliable

Full RAG (chunking, embeddings, pgvector vector search, hybrid search, reranking), structured outputs, function calling, guardrails, model fallback, evaluation via golden sets and LLM-as-judge.

03

Hold production

GCP (Cloud Run, Pub/Sub, Cloud Tasks, BigQuery, monitoring), Docker, Kubernetes, CI/CD, observability, production debugging, absorbing load peaks without cost explosions.

04

Scope and lead

Architecture decisions driven by business stakes, systematic code review, team standards, mentoring, technical scoping from a need expressed in business language. A tech lead who stays in the code.

Track record

2022 - present

ET Technology Consulting (SASU)

Founder - Tech Lead, Backend & AI

Technical consultancy carrying two B2B SaaS products in production: NovoAgent (conversational AI agents for real-estate agencies) and TableAgent (hospitality). Multi-tenant architecture, strong constraints on reliability, GDPR compliance and inference cost.

  • Designed and built a multi-tenant SaaS platform of conversational AI agents, from architecture through to operation.
  • Full RAG pipeline: multimodal document ingestion, chunking, embeddings, vector search on PostgreSQL / pgvector, reranking and retrieval quality evaluation.
  • Hybrid architecture reducing LLM dependency: deterministic routing, semantic cache, business state machine, model calls reserved for cases that warrant them.
  • React Native mobile app embedding non-conversational AI features: speech-to-text, data extraction and structuring, automatic task and reminder creation.
  • WhatsApp Cloud API integration as a Meta Tech Provider: embedded signup, multi-WABA, templates, idempotent webhooks.
  • Agent testing and simulation framework (versioned scenarios, LLM-as-judge) assessing fidelity, compliance and robustness before every release.
  • GDPR and EU AI Act (article 50) compliance, article 28 DPA.
  • Defined code standards and technical scoping for a team of 10 internal and external developers.

Key impact

  • Inference costs divided by 50 between the first version and the optimised architecture, at constant answer quality.
  • Production deployments across some twenty clients in France and French-speaking Switzerland.
  • Significant reduction in first-response time and in the operational load on client teams.

2024 - June 2026

Reveeld

Senior Backend / Full-Stack Engineer - Tech Lead

A dating application with more than 80,000 active users on a GCP cloud-native architecture. Strong constraints on scalability, performance, security and reliability in a consumer environment.

  • Designed and implemented a function-oriented cloud-native architecture on GCP rather than a classic monolithic API.
  • Built backend services in Python and Go, REST and gRPC APIs for inter-service communication.
  • Complete React.js back office (moderation, operations, support, analytics) and contributions to the React Native frontend.
  • PostgreSQL optimisation: queries, schemas, advanced indexing strategies and materialised views.
  • Data / analytics layer via BigQuery and AI workflows (Cloud Vision) for content moderation.
  • Absorbed traffic peaks through asynchronous processing (Pub/Sub, Cloud Tasks) and adaptive Cloud Run scaling.

Key impact

  • Backend stable at 80,000 active users, with load peaks anticipated and no infrastructure cost explosion.
  • Moderation throughput improved by 60 to 70% through automation and AI-assisted workflows.
  • Push notification campaign effectiveness (delivery / CTR) up by 30 to 40%.

2019 - 2024

Yacast

Senior Backend Engineer → Tech Lead

A data intensive environment: music catalogues, TV / radio content recognition and automated reporting for enterprise clients.

  • Designed and delivered backend projects from scratch, weighing REST APIs against gRPC architectures.
  • Technically led the progressive migration of a monolith towards microservices.
  • Designed and optimised ETL pipelines processing millions of rows from music catalogues and broadcast feeds.
  • Music recognition services for TV and radio running continuously (24/7) in a critical environment.
  • Distributed RabbitMQ queues orchestrating asynchronous and parallel processing.
  • Advanced PostgreSQL optimisation, Elasticsearch for concurrent search and Redis caching.
  • Operated a farm of roughly 50 physical servers (≈180 virtual cores each), automated with SaltStack and Ansible.

Key impact

  • API latency reduced by 30 to 40%, some critical SQL queries improved by an order of magnitude.
  • Critical music recognition services kept running 24/7 on large-scale on-premise infrastructure.
  • Reliable delivery of automated reports for several enterprise clients.

Other work

IpOp Tools (Switzerland)

A full-stack web application (Python, PostgreSQL, React) built for a Swiss client: backend architecture, API layers, database optimisation, deployment and operation. Includes an embedded AI layer - RAG over the document base, and an assistant guiding users in expressing and structuring their needs.

For those who dig

Ingestion & pipelines

  • ETL / ELT
  • Python
  • Go
  • Pub/Sub
  • Cloud Tasks
  • RabbitMQ
  • Async processing
  • Idempotency & retry

Warehouse & modelling

  • Advanced PostgreSQL
  • BigQuery
  • Materialised views
  • Indexing strategies
  • Query plans
  • Migrations
  • Historisation

Delivery layer

  • Dashboards
  • Automated reports
  • React / Next.js
  • React Native
  • REST & gRPC APIs
  • Exports & alerts

AI layer

  • RAG
  • pgvector
  • Embeddings
  • Hybrid search
  • Reranking
  • Function calling
  • Structured outputs
  • Guardrails & fallback

Education

2019

Master's degree - Computer Science, specialising in Machine Learning, Artificial Intelligence and Data Science

ESIEA - École Supérieure d'Informatique, Électronique et Automatique, Paris

Where we work

  • Firm based in Marignier, Haute-Savoie - 25 minutes from Geneva.
  • Engagements across France and French-speaking Switzerland, on site and remote.
  • A single technical point of contact, from the first conversation through to operation.
  • Native French, professional English.
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The detailed CV

Full, typeset version in French, ready to print or forward.

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