10+ years
AI/ML LeadershipDelivery across Oceania, Asia, and South America.
AI/ML Leadership CV
Executive profile · recent leadership experience
Diego Marinho de Oliveira
AI/ML Leader | Production AI, Search, Recommendations, and Agentic Systems at Multi-Market Scale
Senior Engineering Manager & Principal AI leading production AI products and platforms across search, recommendations, and agentic experiences in Oceania, Asia, and South America, with results spanning 150+ experiments, sub-30-day launches, and multi-market uplift up to 12x.
Full includes the complete human CV. Executive focuses on summary, leadership highlights, and recent experience.
- 10+ yrs AI/ML
- 150+ A/B tests
- 5–10x recommender gains
- Millions of users
Melbourne, Victoria, Australia · dmarinho.ai@gmail.com
Melbourne, Victoria, Australia · dmarinho.ai@gmail.com
LinkedIn: linkedin.com/in/dmztheone
GitHub: github.com/dmoliveira
150+
Online ExperimentsA/B tests and launches tied to core business metrics.5-10x
Platform GainsRecommender transformation with a 12x record in Asia.Millions
Users ServedReal-time recommendations, AI search, and production AI experiences.
Impact Highlights
Key Outcomes
Executive Summary
- AI/ML leader with 10+ years leading production AI products and platforms across search, recommendations, and agentic experiences in Oceania, Asia, and South America, with results spanning 150+ experiments, sub-30-day launches, and multi-market uplift up to 12x.
- Built the production agent platform behind customer-facing AI search and conversational experiences, standardizing orchestration, evaluation, observability, guardrails, and cost control.
- Delivered measurable business impact through 150+ online experiments, multi-market platform rollouts, and multiple customer-facing AI launches.
- Track record of leading global DS/MLE teams, hiring at scale, and shipping end-to-end production AI systems with strong cost, latency, and reliability controls.
Leadership Highlights
Leadership and Impact
- Unified recommender systems across SEEK markets in ANZ and Asia, delivering 5-10x gains and a 12x peak uplift in one Asia market.
- Led AI initiatives across agentic AI, multi-agent systems, AI search, multimodal CV, and recommendations.
- Launched the RED brand's first GenAI property search within REA in under 30 days, turning natural-language intent into production property discovery and setting the foundation for customer-facing AI journeys.
- Delivered real-time property photo semantic attribute extraction using multimodal LLM/transformers (45+ attributes).
- Shipped multilingual personalized AI search for HK (JobsDB) with +14% improvement on a core metric.
- Repeated the pattern across multiple additional production AI launches, showing the team could scale execution beyond a single flagship program.
Operating at Scale
- Used 150+ online experiments to de-risk ranking, search, and personalization decisions and compound gains on core marketplace metrics.
- Delivered real-time recommendations for millions of users across homepage, email, and push notifications.
- Owned AI platform economics and performance targets across AWS/GCP, using prompt caching, token-level cost tracking, and latency-aware controls to keep production AI practical at scale.
- Designed systems with offline evaluation, online testing, and load tests to meet SLAs (recs ~500ms, GenAI 1-2s).
- Owned AI governance controls across guardrails, rate limiting, evaluation, monitoring, and security validation, including a completed pen test.
- Defined OKRs with executive stakeholders and partnered across product, engineering, and leadership.
Signature Projects
Selected programs and launches representing production AI leadership across agentic systems, search, multimodal computer vision, and recommender platforms.
GenAI Property Search
Launched production AI property search in under 30 days for real user discovery workflows, proving the team could move from concept to customer value quickly. Media
Launch in under 30 daysAgentic AI Platform
Designed and deployed tool-augmented AI agents at scale with orchestration, guardrails, observability, evaluation, and cost controls. Media
Production platform at scaleMultimodal CV Attribute Extraction
Delivered a real-time property photo attribute extraction system using multimodal LLMs and transformers in production.
45+ attributes in productionUnified Recommender Platform
Unified recommender systems across multiple markets with sustained performance gains, creating a repeatable platform rather than isolated market wins.
5-10x gains, 12x recordProfessional Experience
Apr 2024 — Present
- Built and led DS/MLE teams across production Agentic AI, AI search, and personalization (12 people, up to 3 teams), balancing customer value, delivery speed, and platform reuse.
- Launched the RED brand's first GenAI property search end-to-end in under one month to validate conversational discovery in production.
- Launched RED's first conversational AI agent and met SLA 1-2s.
- Built the production agent platform behind AI search and conversational experiences, including orchestration, observability, evaluation, and guardrails.
- Delivered real-time CV attribute extraction and multimodal inference in production.
- Established GenAI observability for real-time monitoring, evaluation, tracing, and operational debugging so teams could ship with clearer reliability and accountability.
- Owned GenAI economics and latency, managing input/output and caching costs while keeping production responses within 1-2s SLAs.
- Owned AI governance controls across guardrails, rate limiting, evaluation, monitoring, and security validation, including a completed pen test.
Mar 2017 — Apr 2024
- Built and led a global DS team of ~10 while hiring across multiple AI teams and expanding the organization's ability to ship search, recommendation, and experimentation systems.
- Led the recommender platform transformation across multiple markets, producing 5-10x improvements and a 12x record in one Asia market.
- Delivered a unified recommender platform across 8+ markets in Asia and ANZ.
- Launched personalized AI search for HK (JobsDB) with +14% main-metric improvement.
- Used 150+ online experiments to de-risk ranking, search, and personalization decisions and compound gains on core marketplace metrics.
- Served millions of users with real-time recommendations across homepage, email, and push notifications.
- Designed systems with offline evaluation, online testing, and load tests to meet SLAs (~500ms for recs).
Jul 2015 — Feb 2017
- Built candidate recommendation systems that delivered >115% uplift on core marketplace metrics.
- Delivered hybrid recommenders combining collaborative filtering and content-based methods.
Early Career Roles (Grouped)
2009 — 2015 - Machine Learning Engineer, RBS Group - Appus Startup. - Machine Learning Specialist, Zunnit Technologies. - Software Engineer, Vale Mining / Visagio. - Software Analyst, TOTVS. - Delivered AI systems across startups and enterprise companies in South and Southeast Brazil.
Selected Impact
- 150+ online experiments improving core business metrics.
- 10+ AI projects shipped to production at REA.
- Real-time CV system extracting 45+ property attributes.
- Multi-market recommender unification across 8+ markets.
- Delivered real-time recommendations and AI search to millions of users.
Education
2010 — 2012
- Focus: NLP/NER; FS-NER research (see Publications).
- Top student with 90%+ average.
2006 — 2009
- Student Medal for Excellence; 90%+ average.
- Research in ANN, tutoring, and internships.
Awards (Selected)
- Winner, REA Hackdays 2024 — Hack it Forward Award for Community, Social & Environmental Good (2024)
- Top performer, Databricks Gen-AI Australia Cup (2023)
- Winner, SEEK International Volponi Award (2017)
- Student Medal for Excellence, B.Sc. Computer Science (2009)
Publications (Selected)
- Imbalance Data Sparsity as Source of Unfair Bias in Collaborative Filtering (RecSys 2022)
- Offline Evaluation Standards for Recommender Systems (RecSys 2021)
- FS-NER: Filter-Stream Named Entity Recognition on Twitter Data (WWW Workshop 2013)
- Applying Neural Networks to Determine the Socio-Environmental Factors Responsible for Potable Water Consumption (SMC 2010)
- The Usage of Artificial Neural Networks in the Classification and Forecast of Potable Water Consumption (IJCNN 2009)
- New Perspectives in the Context of Computer Architecture – Educational Software for Parallel Computation (WEAC 2009)
Skills
- Leadership: org design, hiring, mentoring, cross-functional strategy.
- AI/ML Systems: ranking, retrieval, AI search, hybrid search, reranking, search relevance, semantic search, sequential recommenders, online experimentation, A/B testing, learning-to-rank.
- GenAI/LLMs: agentic AI, AI agents, Agentic Engineering, tool calling, stateful agents, memory, RAG, structured outputs, embeddings, evaluation, fine-tuning (QLoRA), MCP.
- ML Techniques: transformers, embeddings, matrix factorization, clustering, RL, offline evaluation.
- Cloud Platforms: GCP, Vertex AI, AWS, BigQuery.
- Tools: PyTorch, TensorFlow, scikit-learn, Pandas, Spark, FastAPI.
Core Expertise
AI/ML leadership across search, recommendations, and agentic systems; building production GenAI and LLM platforms with hybrid search, reranking, retrieval, tool calling, stateful agents, RAG, observability, evaluation, safety, guardrails, AI governance, and MLOps discipline.