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I am a Senior ML Software Engineer with 11 years of experience building production AI/ML systems, including large language models, retrieval-augmented generation, and multimodal models. I design and operate high-availability microservices that power customer-facing AI applications in hybrid environments. I specialize in deploying and iterating models with robust evaluation pipelines, monitoring, and performance tuning. I have delivered compliant data platforms across healthcare, energy, and public sector domains, and I excel at technical leadership, debugging in production, and partnering with stakeholders to ship reliable AI.…I am a Senior ML Software Engineer with 11 years of experience building production AI/ML systems, including large language models, retrieval-augmented generation, and multimodal models. I design and operate high-availability microservices that power customer-facing AI applications in hybrid environments. I specialize in deploying and iterating models with robust evaluation pipelines, monitoring, and performance tuning. I have delivered compliant data platforms across healthcare, energy, and public sector domains, and I excel at technical leadership, debugging in production, and partnering with stakeholders to ship reliable AI.

Carlo Reyes

Data Scientist, AI Engineer, AI Developer, +5

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I am a Senior ML Software Engineer with 11 years of experience building production AI/ML systems, including large language models, retrieval-augmented generation, and multimodal models. I design and operate high-availability microservices that power customer-facing AI applications in hybrid environments. I specialize in deploying and iterating models with robust evaluation pipelines, monitoring, and performance tuning. I have delivered compliant data platforms across healthcare, energy, and public sector domains, and I excel at technical leadership, debugging in production, and partnering with stakeholders to ship reliable AI.…I am a Senior ML Software Engineer with 11 years of experience building production AI/ML systems, including large language models, retrieval-augmented generation, and multimodal models. I design and operate high-availability microservices that power customer-facing AI applications in hybrid environments. I specialize in deploying and iterating models with robust evaluation pipelines, monitoring, and performance tuning. I have delivered compliant data platforms across healthcare, energy, and public sector domains, and I excel at technical leadership, debugging in production, and partnering with stakeholders to ship reliable AI.

Available to hire

I am a Senior ML Software Engineer with 11 years of experience building production AI/ML systems, including large language models, retrieval-augmented generation, and multimodal models. I design and operate high-availability microservices that power customer-facing AI applications in hybrid environments.

I specialize in deploying and iterating models with robust evaluation pipelines, monitoring, and performance tuning. I have delivered compliant data platforms across healthcare, energy, and public sector domains, and I excel at technical leadership, debugging in production, and partnering with stakeholders to ship reliable AI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior Data Scientist at Cognizant

April 1, 2020 - Present

Led AI training and review workflow for LLM responses using Python, SQL, and rubric-based guidelines; improved annotation QA consistency and feedback quality; resolved ambiguous cases with decisive reviewer notes. Built GraphRAG pipelines with Neo4j, embeddings, and semantic search to retrieve tax guidance from large PDF corpora; enabled faster reviewer decisions and more reliable outputs aligned to policy constraints. Designed response rating criteria and sampling plans using statistics and A/B testing to compare prompts, track drift, and communicate results to cross-functional stakeholders. Implemented document processing for complex PDFs with Python, OCR-style parsing, and hierarchy extraction; produced normalized JSON outputs supporting labeling, audit trails, and downstream LLM training. Created cross-document entity linking with NER, Cypher queries, and NetworkX traversal to reduce duplicates and improve context for RAG evaluations during edge-case reviews. Shipped a multi-agent

Machine Learning Engineer at Attain Partners

May 1, 2018 - April 1, 2020

Built data labeling and QA routines for recommendation training datasets; improved consistency of session events and reduced noisy labels through audit samples and decisive reviewer feedback. Developed content-based recommenders with KNN, feature engineering, and embeddings; validated results with A/B testing to explain trade-offs for product decisions. Implemented collaborative filtering via neural matrix completion in PyTorch and optimized offline evaluation with cross-validation, ensuring repeatable scoring and clear error analysis for stakeholder reviews. Created session-based next-click prediction using Transformer models in PyTorch; documented dataset curation to ensure consistent application of definitions across experiments. Delivered healthcare IoT predictive maintenance models using gradient boosting and outlier detection; deployed ML pipelines in Azure ML and Databricks with automated training runs and monitoring to catch data quality issues early. Built an advertisement opt

Data Scientist at Maximus

June 1, 2017 - April 1, 2018

Built supervised learning models for program analytics, combining feature engineering with clear label definitions to maintain training data quality. Performed statistical analysis, hypothesis testing, and regression modeling to evaluate policy changes; produced memos that explained assumptions and actionable recommendations. Implemented text classification and NER baselines, conducted error analysis, and updated labeling guidance to align ambiguous language interpretations. Created repeatable ETL pipelines in Bash, Python, and PostgreSQL to improve data completeness checks and enable faster sampling for QA. Designed cross-validation and model selection routines for classification tasks; documented metrics and edge cases for consistent evaluation. Supported content review workflows by building SQL dashboards and templates for auditing records and tracing decisions back to source data. Coordinated with SMEs to resolve conflicting definitions and standardize taxonomy for labeling and rep

Junior Data Scientist at ITS

April 1, 2016 - January 31, 2017

Prepared and cleaned datasets using Python, pandas, and SQL with well-documented assumptions. Built baseline classification and regression models, and communicated results in clear written language for non-technical stakeholders. Assisted with data labeling, defined categories, and flagged edge cases early to enable decisive guidance from senior staff. Maintained small ETL scripts in Bash and Python for data pulls, supporting timely sampling for QA. Ran outlier detection and data validation checks in SQL, escalating anomalies with evidence and suggested fixes. Supported model evaluation with train/test splits and metric tracking, ensuring transparent reporting. Contributed to simple REST data pulls and JSON parsing; assisted with basic indexing requests and query tuning in PostgreSQL. Created training guides for interns on labeling rules and QA checks, improving consistency and reducing rework.

Senior ML Software Engineer at Cognizant

April 1, 2020 - Present

Owned architecture and technical direction for production AI/ML platforms, mentoring 6–10 ML and data engineers while aligning delivery to customer needs and enterprise constraints in hybrid environments. Built and iterated on Generative AI systems using LLMs (BERT, GPT family) for multilingual NLP and automation workflows, focusing on production reliability, measurable evaluation, and safe rollout practices. Designed and implemented RAG platforms grounding responses with external data, improving accuracy and reducing hallucinations. Developed and maintained evaluation pipelines for LLM and NLP systems, including offline test sets and continuous checks. Implemented monitoring and observability for ML services to detect data/model drift and speed up debugging. Engineered scalable ML/LLM inference microservices in Python (FastAPI/Flask) with RESTful APIs for high-throughput, low-latency predictions. Led multimodal systems for digital inking using vision-language models. Customized mult

Senior ML Software Engineer at Attain Partners

May 1, 2018 - April 30, 2020

Spearheaded engineering and deployment of retrieval-based NLP systems using semantic search, vector embeddings, and Elasticsearch indexing. Architected ML backend services in Python (FastAPI) within a microservices architecture. Built production-grade RESTful APIs for real-time prediction serving with high availability. Established automated ML pipelines for ingestion, feature engineering, training, and validation using MLflow and Apache Airflow. Deployed containerized ML services using Docker on AWS (EKS/ECS). Optimized inference pipelines for real-time analytics, reducing end-to-end latency by 35%. Delivered an end-to-end deep learning solution for NILM to support predictive energy analytics. Conducted AB testing and statistical analysis to validate model effectiveness.

Data Scientist / Machine Learning Engineer at Maximus

January 1, 2017 - April 30, 2018

Developed and deployed computer vision models in TensorFlow/Keras to analyze RGB-IR satellite imagery for precision agriculture decision support and yield forecasting. Engineered ML workflows using sensor data and NDVI for near-real-time monitoring. Built feature engineering pipelines to process large volumes of imagery and sensor data. Applied statistical modeling to improve robustness on environmental datasets. Integrated deployed models into production monitoring systems for real-time inference and issue triage. Maintained model training and deployment scripts and tuned models based on field feedback.

Machine Learning Researcher at ITS

April 1, 2016 - January 31, 2017

Implemented probabilistic and Bayesian modeling approaches in Python, improving prediction accuracy. Designed multimodal ML experiments focusing on representation learning. Developed algorithms using NumPy and SciPy for high-dimensional modeling. Built an experimental benchmarking framework and validated results with statistical significance testing. Collaborated to troubleshoot model behavior and data issues.

Machine Learning Intern at Audacious Inquiry

July 1, 2015 - March 31, 2016

Developed a multi-view learning framework in Python and Scikit-learn to combine multiple data streams and improve predictive performance. Implemented a Mixture of Experts approach to specialize models across input types. Performed data preprocessing and feature engineering with Pandas and NumPy. Conducted model evaluations and documented findings to support experimentation.