Applied AI & ML
Statistical reasoning, computer vision and multimodal learning. Connecting data strategy and model development to a concrete engineering need.
Applied AI Lead · AI Systems · Automation · Multimodal AI
Evelyn Gutierrez, PhD
Complex problems.
Practical AI systems.
I turn complex and ambiguous problems into practical AI solutions.
With more than a decade across data, machine learning and applied AI, I work from problem framing and experimentation through system design, prototyping, validation and production-oriented engineering.
A CONNECTED TRAJECTORY
Statistics → Machine learning → Computer vision → Industrial AI → GenAI → AI systems
01 / Expertise
Depth in AI.
A view of the whole system.
Choosing the right approach means understanding the problem, the evidence and the environment where a solution needs to work.
GenAI, RAG & Agents
Technical knowledge assistants, retrieval systems and agent workflows. Building prototypes and evaluating their answers against domain expectations.
AI Systems & Integration
AI systems design with attention to APIs, data flows, evaluation and production constraints. Collaborating across software, cloud and architecture teams.
Technical Direction
Turning ambiguous needs into feasible approaches. Comparing options, defining prototypes and using expert feedback to guide technical decisions.
02 / How I work
From the real need
to a usable system.
I contribute across the AI lifecycle, with evidence and domain feedback shaping each next step.
- 01
Understand
Clarify the users, constraints, available data and success criteria.
- 02
Structure
Turn ambiguity into a tractable problem and identify the highest-value use cases.
- 03
Design
Compare approaches and define components, interfaces and evaluation strategy.
- 04
Build
Develop pipelines, models, agents, APIs and demonstrators hands-on.
- 05
Validate
Measure performance and collect expert feedback against realistic needs.
- 06
Industrialize
Work with engineering teams on reproducibility, integration and operational constraints.
An iterative process: what we learn in validation informs what we build next.
03 / Selected work
Applied problems.
Evidence that matters.
Selected contributions across industrial AI, knowledge systems and research. Industrial projects are anonymized.
01 / Industrial computer vision
Making visual inspection
work on the production line.
Computer-vision quality inspection in an industrial environment, from requirements and annotation strategy to model development and production validation.
>95%on relevant evaluation metrics
~1/minvehicle cadence in the production context
Several classes reached around 99%. Results are class- and metric-dependent; line cadence describes the operating context, not measured model latency.
Approach & contributions
Developed object-detection models using RetinaNet, supported by annotation strategy and semi-automatic relabeling.
Investigated false positives and underrepresented classes, introduced class-specific thresholds, and used out-of-time testing alongside production validation.
Focus: evaluating the system against the realities of the production data, not only an aggregate model score.
02 / Technical knowledge systems
Turning technical documents
into usable knowledge.
A RAG prototype for answering questions from technical documentation, connecting document ingestion, retrieval and answer evaluation.
~93.1%validated answers in the prototype evaluation
24 PDFsprocessed into 234 chunks
A prototype result on its evaluated document set, not a general accuracy claim or production guarantee.
Approach & contributions
Built the ingestion, embedding and retrieval flow with vector-database support, then evaluated answers for the technical knowledge use case.
Used validation to assess whether retrieved information could support useful answers for the intended users.
Focus: treating answer evaluation as part of the system, rather than stopping at a working demo.
03 / 3D & geospatial systems
From heterogeneous data
to digital-twin pipelines.
Large-scale 3D and geospatial processing across LiDAR, terrain, buildings, OSM and satellite data, translating research methods into modular operational pipelines.
Approach & contributions
Developed distributed processing with Databricks, PySpark and Delta Lake, supported by PostgreSQL/PostGIS data layers and containerized Azure workloads.
Evaluated accuracy, compute-cost and processing-time trade-offs; introduced logging, metrics, traceability and reproducibility practices while collaborating with simulation, product and cloud teams.
Focus: systems thinking across data scale, technical trade-offs and the transition from research to engineering.
04 / Medical imaging & GenAI
Structuring emerging AI
into rigorous R&D.
Applied research and engineering for medical imaging and synthetic-data generation, from dataset and literature analysis to evaluation design and deployment considerations.
Approach & contributions
Designed end-to-end experimental pipelines and explored conditional generative approaches combining diffusion models and Gamma-VAE concepts.
Built agentic research assistants for scientific, market, funding and dataset discovery, while mentoring junior engineers and communicating results to technical and non-technical audiences.
Focus: turning emerging methods into structured engineering work with explicit evidence and maturity milestones.
05 / Applied AI discovery
Deciding what
is worth building.
Working with domain experts to map workflows, identify practical AI opportunities and build focused demonstrators for early validation.
Approach & contributions
Explored engineering-training knowledge assistants, technical-publication automation and AI-assisted FPGA development.
Selected approaches based on the need—including semantic search and deterministic document checking—rather than defaulting to an LLM.
Focus: reducing ambiguity and testing the highest-value assumption before committing to a larger implementation.
06 / Multimodal computer vision research
Bringing geometry
and temperature together.
Dual-PhD research combining RGB, thermal imaging and 3D reconstruction for chronic wound monitoring, supported by deep learning and medical-image analysis.
RGB + THERMAL + 3D
Designed the research and acquisition-to-visualization pipeline, coordinated international clinical data collection involving more than 4,000 images, and published peer-reviewed results.
Explore the doctoral research Read the 3D wound segmentation publication →04 / Experience & direction
A broader scope.
The same problem-solving thread.
Quantitative foundations, research depth and applied engineering—now converging in AI systems and technical direction.
2023–Present
Applied Scientist / Senior Data Scientist · Expleo Group
Applied AI technical contributor and lead across R&D, innovation and industrial projects. Work spans problem framing, system design, hands-on prototypes, evaluation, deployment-oriented engineering and technical mentoring.
Current focus includes industrial computer vision, 3D and geospatial systems, medical imaging, GenAI, RAG, agents and workflow automation—with increasing responsibility for technical trade-offs and the transition toward usable systems.
See selected contributions2019–2023Dual PhD · 2023
3D computer vision & multimodal imaging
Doctoral work across computer science and engineering: combining 3D models, thermal imaging and deep learning for chronic wound assessment, while coordinating clinical data acquisition and supervising students.
Université d’Orléans · Pontificia Universidad Católica del Perú
Earlier experience2011–2018
Statistics, machine learning & decision support
Credit risk modeling, geospatial analysis and consulting. Building a foundation in quantitative reasoning, varied data sources and applied business problems.
Earlier roles
- Data Scientist — Credit Risk Modeling Specialist
LenddoEFL · March 2015–October 2018 - Geo-Intelligence Consulting Analyst
Business Analytics SAC · January 2014–March 2015 - Credit Risk Analyst
Entrepreneurial Finance Lab · September 2011–December 2013
05 / Capabilities
Tools follow
the problem.
Breadth is useful when it supports a better decision. I select and combine tools around the problem, evidence and system constraints.
AI & machine learning
Statistical ML · Computer vision · Deep learning · Multimodal AI · LLMs · RAG · Agents
Python / PyTorch / TensorFlow / OpenCV / Open3D
AI engineering
APIs · Evaluation pipelines · Workflow automation · Retrieval systems
FastAPI / Docker / GitLab CI/CD / CrewAI / LlamaIndex / ChromaDB / Ollama
Data & platforms
Relational and vector databases · Distributed and geospatial processing · Cloud and data platforms
SQL / PostgreSQL / PostGIS / Azure / Databricks / PySpark / Delta Lake / GeoPandas / QGIS / ArcGIS / AKS exposure
06 / Research & publications
Scientific depth.
Applied perspective.
My research background brings rigor to experimentation, model evaluation and the interpretation of results.
Education
Two disciplines.
A shared research question.
- PhD in Computer Science
- Université d’Orléans
- PhD in Engineering
- Pontificia Universidad Católica del Perú
- MSc in Statistics
- Pontificia Universidad Católica del Perú
- BSc in Statistical Engineering
- Universidad Nacional de Ingeniería, Peru
Computer vision · Deep learning
Multi-View Data Augmentation to Improve Wound Segmentation on 3D Surface Model by Deep Learning
Read publication Multimodal imagingMultimodal and Multiview Wound Monitoring with Mobile Devices
Read publication Thermal imaging · Clinical monitoringCorrection of Temperature Estimated from a Low-Cost Handheld Infrared Camera for Clinical Monitoring
Read publication
Evelyn GutierrezStatistics → AI → Systems
07 / About
I enjoy problems that require
both analysis and building.
I’m an Applied AI Lead with more than a decade across data, machine learning and AI. The common thread is turning complex problems into practical systems.
My work spans structured and geospatial data, computer vision, 3D and multimodal AI, medical imaging, distributed data processing, GenAI, RAG and agents. That breadth lets me choose an approach based on the problem rather than force every problem into one specialty.
I bring architecture awareness to AI systems design and technical discussions. I have designed modular pipelines and interfaces and worked with cloud, containers and CI/CD; I’m actively deepening production AI architecture, observability, reliability, security, serving and lifecycle operations.
Spanish · English · French
08 / Let’s connect
A complex AI problem?
Let’s find a way forward.
For conversations about applied AI, AI engineering and building useful systems.
Get in touch