Albert Dulout

Albert Dulout

Applied AI Engineer

Azure-focused Applied AI Engineer in Singapore with 3+ years delivering document intelligence, optimization and cloud data systems from rapid PoCs to production platforms. I own backend, AI, infrastructure and handover, with additional hands-on experience across AWS, including Government Commercial Cloud (GCC) environments, GCP and Cloudflare.

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Selected work

Four systems that show how I scope, build and deliver applied AI and data products.

AI Workflow PoCs

Enterprise client delivery · 4 PoCs · 2025

Built the backend, AI pipelines and Azure foundations for four seven-day enterprise document-review PoCs. One was selected for a company-wide MVP, with my backend, AI and cloud architecture handed over to the delivery team. In a team of two engineers and one designer, I owned ingestion, OCR, retrieval, structured comparison and reviewer-verifiable outputs.

Stage
4 PoCs · 1 selected for company-wide MVP
Delivery
~2 weeks per PoC
Team
2 engineers · 1 designer
Engineering details Retrieval, review safeguards and delivery
  • Backend and AI. Designed FastAPI services and AI pipelines for OCR, parsing, embeddings, hybrid retrieval and schema-constrained comparison. I also owned API management, container deployment and CI/CD, while teammates built most React interfaces.
  • Regulatory comparison. Used exact and fuzzy prefilters before batched LLM verification.
  • Loan review. Combined hierarchical chunks, BM25, HNSW vector search and semantic reranking.
  • Reviewer outputs. Produced annotated PDFs, clause trackers, ranked candidate reports and Excel exports.
  • Built with. Python, FastAPI, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure AI Document Intelligence and Vision OCR, API Management, Container Apps, Static Web Apps, Docker and GitHub Actions.

Waste-collection route optimization

Client delivery · Operations research · 2025

Delivered a waste-collection planning platform in about six weeks for operations across two countries. As the sole engineer, I built the OR-Tools solver, resilient workbook ingestion, React interface, operational reporting and Azure deployment. The system models real operating constraints and returns unserved stops explicitly when a scenario is infeasible.

Scope
Operations across 2 countries
Delivery
~6 weeks
Team
Sole engineer
Engineering details Constraints, trade-offs and reporting
  • Solver. Modelled capacity, service time, shifts, depots, unloads and objectives including time, distance, cost, coverage and profit.
  • Repeat trips. Represented repeat trips with shift-aware virtual vehicles.
  • Distance matrices. Supported Haversine, cached OSRM and Google route matrices to balance road realism, cost, rate limits and offline operation.
  • Operational workflows. Owned the FastAPI backend and React interface, including tolerant XLSX/CSV parsing and PDF/XLSX reports.
  • Forecasting extension. Implemented a moving-average baseline behind a replaceable forecasting interface designed for future Prophet or XGBoost models.
  • Built with. Python, FastAPI, Google OR-Tools, React, pandas, OSRM, Google Routes, Docker and Azure Container Apps.

Governed workforce and finance data platform

Internal production platform · PALO IT · 2026

Built and deployed a governed Azure data platform that syncs workforce and finance data into PostgreSQL for Internal, HR, Finance and Audit users. As the sole engineer over three months, I owned the architecture, ingestion, access model, infrastructure and production operations.

Stage
Internal production
Scope
10 sync domains · 4 role-based access groups
Ownership
Sole engineer · ~3 months
Engineering details Data integrity, identity and recovery
  • Azure ownership. Owned the platform from infrastructure design through production deployment, provisioning dev and production resource groups, Functions, PostgreSQL, Key Vault and monitoring with Bicep.
  • Access control. Created Microsoft Entra security groups for Internal, HR, Finance and Audit access, then configured managed identities and scoped RBAC for runtime and deployment.
  • Data reliability. Durable Functions coordinate ten sync domains; checkpoints and completeness gates let interrupted imports resume safely without treating partial source data as deletions.
  • Interfaces. Built role-scoped read APIs and an MCP interface for workforce queries.
  • Built with. TypeScript, Azure Functions, Durable Functions, PostgreSQL, Drizzle, Entra ID, Bicep, GitHub Actions OIDC and Azure Monitor.

Mathilde Recipes: AI-assisted publishing platform

Public personal project · 2026–present

Open live demo

Built and operate a public collection of 400+ recipes from scanned documents and phone photographs. The French-first platform adds English translation, search, a recipe-aware assistant, nutrition estimates and protected review workflows while keeping most infrastructure within Cloudflare's free tiers.

Public proof
400+ published recipes
Operations
Scheduled Dropbox ingestion
Ownership
Sole builder and maintainer
Mathilde Recipes gallery showing recipe photographs, search and category filters.
Live French-first recipe gallery and recipe-aware assistant
Engineering details Ingestion, quality controls and operations
  • Ingestion. Extracts text or image content and requests structured model output.
  • Quality controls. Validates provenance and tags, then removes mirrored and fingerprint duplicates.
  • Publishing. Rebuilds a deterministic catalog and records skip reasons and validation failures instead of silently publishing.
  • Asynchronous operations. Uses queued image generation and protected admin routes to separate long-running and review-sensitive work from the public request path.
  • Built with. React, TypeScript, Cloudflare Pages and Functions, D1, Vectorize, R2, Queues, Gemini, OpenAI Image 2 and GitHub Actions.

Capabilities

Core technologies used across selected work and additional professional projects.

Applied AI and ML
RAG · agentic AI and MCP · structured extraction · multimodal/OCR workflows · evaluation · PyTorch · Hugging Face · LoRA · MLflow
Retrieval and document intelligence
Azure AI Search · Azure AI Document Intelligence · embeddings · BM25 · HNSW · semantic reranking
Backend, data and optimization
Python · TypeScript · SQL · FastAPI · React · PostgreSQL · pandas · Google OR-Tools
Cloud platforms
Azure primary and deepest experience · AWS, including Government Commercial Cloud (GCC) · GCP · Cloudflare
Cloud engineering and delivery
Azure AI Foundry · Functions · Container Apps · API Management · Bicep · Terraform · Docker · GitHub Actions OIDC · managed identity and RBAC · Azure Monitor

Experience

3+ years delivering applied AI and cloud systems, preceded by analytics roles at Papernest and Nestlé.

PALO IT

Singapore

Data Scientist, Innovation Lab
February 2025–present
Junior Data Scientist
March 2023–January 2025
  • Deliver applied AI systems across document intelligence, forecasting, optimization and cloud data platforms from technical discovery through deployment and handover, primarily on Azure, with additional project experience on AWS, including GCC environments.
  • Built and evaluated cash-flow forecasting models during a one-month project using PyTorch, with MLflow for experiment tracking and model comparison.
  • Applied LoRA fine-tuning in separate projects and served open-source models with Hugging Face Text Generation Inference.
  • Created a reusable Azure Dev Center catalog and Vercel-style self-service deployment path for Container Apps, PostgreSQL, Static Web Apps and AI/RAG services. Bicep, managed identities, scoped RBAC and GitHub OIDC standardized delivery and enabled a designer to publish AI-assisted mockups as live client demonstrations without managing the underlying Azure infrastructure.
  • Translate client needs into technical options and proposals, size delivery teams and infrastructure, and mentor Innovation Lab engineers.

Analytics Engineer Intern

Papernest · Barcelona · February–August 2022

  • Increased successful Google Ads conversion imports by 6% by tracing and restoring records dropped across BigQuery-based SQL and ETL pipelines.
  • Automated campaign alerts and maintained reporting used for weekly spend decisions.

Master Data Analyst Intern

Nestlé Europe · Paris · June–December 2021

  • Built an Excel/VBA pricing simulator on SAP and SQL data so commercial teams could test scenarios independently; also automated recurring data-management work.

Education and credentials

Engineering Degree / MSc, Data Science and Optimization

IMT Atlantique · France · 2019–2023

Coursework included machine learning, computer vision, natural language processing, time-series forecasting, operations research, optimization algorithms and big-data architecture.

Preparatory Class for the Grandes Écoles

Lycée Montaigne · Bordeaux · 2016–2019

Beyond the work