Albert Dulout

Albert Dulout

Applied AI Engineer, Singapore

I build document-intelligence and optimisation systems on Azure, from prototype to production, often as the only engineer between raw data and a deployed service. I turn ambiguous operational problems into dependable software.

Based in Singapore since 2023, currently building applied AI at PALO IT’s Innovation Lab.

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Selected work

Enterprise AI, public-sector ML, operations research, internal platforms and a live public product.

Ordered by relevance, not date. Client names are used only where the work was publicly announced; all other engagements are described in generalised terms.

Four document-review pilots; the client took one to a company-wide MVP

Enterprise client · Singapore · 4 PoCs · 2025

Built the backend, AI pipelines and Azure foundations for four rapid enterprise document-review PoCs. The HR-screening pilot cut internship-candidate ranking from half a day to minutes (the client’s own estimate); recruiters reviewed every ranking and the system never rejected a candidate. One PoC was selected for a company-wide MVP and handed to the delivery team. Across the workflows, outputs preserved source text or page references for verification.

Stage
4 PoCs · 1 selected for company-wide MVP
Delivery
~2 weeks per PoC
Team
2 engineers · 1 designer

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.

How review stayed verifiable
  • 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.
  • Evaluation. Measured retrieval against hand-labelled clause pairs, tracking prefilter precision and agreement between LLM verification and reviewer ground truth.
  • Candidate screening. Removed direct identifiers before model review and tested ranking sensitivity to candidate order; recruiters made every decision on the ranked shortlist.
  • Cost. Sized each workflow to the cheapest service that met its latency and governance needs: Container Apps rather than AKS, and pgvector rather than managed search where retrieval latency was not critical.
  • Reviewer workflow. Produced annotated PDFs, clause trackers, ranked candidate reports and Excel exports so reviewers could inspect findings against the source material.

Interactive urban visualisations for a national planning exhibition

Urban Redevelopment Authority · public exhibition · 2024–2025

Built the interactive generative-AI exhibit for Enabling Sustainable Growth: Shaping the Future of Work, part of URA’s Draft Master Plan 2025 public-engagement programme. Visitors selected amenities and design features on a tablet and received on-demand visualisations of possible future business nodes. Launched by the Senior Minister of State for National Development and Digital Development and Information, the exhibition then ran free to the public at The URA Centre for ten weeks.

Stage
Public exhibition · 24 Oct 2024–3 Jan 2025
Delivery
~10 days to build
Latency
~6 s median generation, held under 10 s to display

Built with. Python, state-of-the-art image-generation models, LoRA fine-tuning, model-based upscaling and a tablet-facing interactive front end.

Coverage. URA press release · PALO IT project announcement

How the visual style stayed consistent
  • Style development. Generated several hundred candidate images across keyword combinations, then trained a LoRA on the images chosen by URA’s architect so live output held one coherent visual style rather than drifting between visitors.
  • Latency. Generated at reduced resolution in about six seconds, the floor for the state-of-the-art models that met the quality bar, then upscaled with a separate model while visitors moved from the tablet to the display, holding the whole interaction inside a ten-second budget.
  • Constraint. Built in about ten days ahead of a fixed public launch date.
  • Live support. Supported the installation across its ten-week public run, including on-site visits to diagnose and fix faults while the exhibition stayed open.

Routing waste collection across two countries

Client delivery · Operations Research · 2025

Delivered a waste-collection planning platform in about six weeks for operations across two countries. As sole engineer, I turned inconsistent client workbooks into validated scenarios and delivered a data-quality assessment that changed the client’s benchmarking plan, identifying the gaps they needed to close before any comparison with their manual baseline was meaningful. The solver modelled capacity, service time, shifts, multi-depot pickups and unloads, and returned unserved stops when a scenario was infeasible. Scenarios were pilot-scale (~40 stops per truck); the solver and data layer were built for multi-depot fleets and repeat trips.

Delivery
~6 weeks · sole engineer
Model
Multi-depot, shift- and capacity-constrained · two countries
Outcome
Data-quality assessment that changed the client’s benchmarking plan

Built with. Python, FastAPI, Google OR-Tools, React, pandas, OSRM, Google Routes, Docker and Azure Container Apps.

How routing constraints worked
  • 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. Shipped a moving-average baseline behind a swappable forecasting interface, so Prophet or XGBoost models can drop in without touching the API.

A governed workforce-and-finance data platform replacing monthly reconciliation

Internal production platform · PALO IT · 2026

Built and deployed a governed Azure data platform that synchronised workforce and finance data into PostgreSQL, and it has run in production since. It replaced a recurring monthly reconciliation that consumed roughly a person-day across two teams, ending manual timesheet follow-up and spreadsheet-based billing-exception overrides. Checkpoints, completeness gates and least-privilege access protected data integrity and access.

Stage
Internal production
Outcome
~1 person-day of monthly reconciliation removed
Ownership
Sole engineer · ~3 months

Built with. TypeScript, Azure Functions, Durable Functions, PostgreSQL, Drizzle, Entra ID, Bicep, Power Automate, GitHub Actions OIDC and Azure Monitor.

What production required
  • 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. Entra ID with managed identities and least-privilege role scoping across runtime and deployment, with access separated by function.
  • Data reliability. Used Durable Functions to coordinate ten sync domains; checkpoints and completeness gates allowed interrupted imports to resume safely without treating partial source data as deletions.
  • Finance access. Built finance-facing views and role-scoped read APIs that replaced manual exception calculations, plus an MCP interface for workforce queries.
  • Process improvement. Used the governed data to specify an automated reminder flow that closed the timesheet gaps driving the monthly rework, implemented in Power Automate by a colleague.
  • Development and access. Built a mock API server for the development environment and kept its dataset synchronised with production, with a request-and-approval workflow for access.

Digitising 400+ family recipes

Live personal platform · 2026–present

Open live demo

Built and now operate a live public platform holding 400+ recipes digitised 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.

Stage
Live · 400+ published recipes
Operations
Scheduled Dropbox ingestion
Ownership
Sole builder and maintainer

Built with. React, TypeScript, Cloudflare Pages and Functions, D1, Vectorize, R2, Queues, Gemini, gpt-image-2 and GitHub Actions.

Mathilde Recipes gallery showing recipe photographs, search and category filters.
Live French-first recipe gallery and recipe-aware assistant
How the publishing pipeline works
  • Ingestion. Extracts text or image content and requests structured model output.
  • Quality controls. Validates provenance and tags, then removes mirrored and fingerprint duplicates. Extraction is spot-checked against the scanned originals, and failures are routed to review rather than published.
  • Publishing. Rebuilds a deterministic catalogue 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.

Experience

Delivering applied AI and cloud systems since 2023, preceded by analytics internships at Papernest and Nestlé. Titled Data Scientist; in practice the scope has been end-to-end engineering: pipelines, APIs, infrastructure and deployment. Much of it sits in document-heavy, finance-adjacent workflows: loan review, cash-flow forecasting, billing and workforce data.

PALO IT

Singapore

Data Scientist, Innovation Lab (promoted Feb 2025)
Feb 2025–present
Junior Data Scientist
Mar 2023–Jan 2025
  • Enabled a designer to ship AI-assisted mockups without touching Azure infrastructure or holding subscription-level access.
  • Built a self-service Azure Dev Center catalogue for Container Apps, PostgreSQL, Static Web Apps and AI/RAG services, using Bicep, managed identities, scoped RBAC and GitHub OIDC so developers could provision guarded environments themselves.
  • Delivered applied AI systems across document intelligence, forecasting, optimisation and cloud data platforms from technical discovery through deployment and handover, primarily on Azure.
  • Worked on AWS, including Singapore’s Government Commercial Cloud (GCC) environments.
  • Built and evaluated cash-flow forecasting models during a one-month project using PyTorch, with MLflow for experiment tracking and model comparison.
  • Shipped both solo and as part of a delivery team, including handing a selected prototype to the team that productised it, and mentored Innovation Lab engineers.
  • Translated client needs into technical options and proposals, and sized delivery teams and infrastructure.

Analytics Engineer Intern

Papernest · Barcelona · Feb–Aug 2022

  • Recovered Google Ads conversion records being silently dropped across BigQuery-based SQL and ETL pipelines, increasing successful imports by 6%.
  • Automated campaign alerts and maintained reporting used for weekly spend decisions.

Master Data Analyst Intern

Nestlé Europe · Paris · Jun–Dec 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.

Skills

LLM and retrieval
Azure OpenAI, Azure AI Foundry, Azure AI Search, Document Intelligence and Vision OCR, hybrid BM25 and vector retrieval, pgvector, semantic reranking, schema-constrained extraction, retrieval evaluation
Optimisation and forecasting
Google OR-Tools vehicle routing, capacity, shift and multi-depot constraints, PyTorch, MLflow, Prophet, XGBoost, pandas
Cloud and platform
Azure Functions and Durable Functions, Container Apps, API Management, Static Web Apps, Key Vault, Azure Monitor, Entra ID, Bicep, Docker, GitHub Actions and OIDC, PostgreSQL, Cloudflare Workers, D1 and R2, AWS including GCC
Languages
Python, TypeScript, SQL
Ways of working
Technical discovery, proposal and estimation, sole ownership through to production, handover to delivery teams, mentoring

Education and credentials

Diplôme d’ingénieur (Master of Science in Engineering equivalent), Data Science and Optimisation

IMT Atlantique · France · 2019–2023

French engineering degree combining advanced mathematics with applied machine learning and operations research, entered through a competitive three-year preparatory programme.

Preparatory Class for the Grandes Écoles

Lycée Montaigne · Bordeaux · 2016–2019

Intensive mathematics and physics programme for France’s competitive engineering-school entrance exams.

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