About The Data Sensei
We build production-grade AI and data systems that ship—and stay operational.
Who We Are
The Data Sensei deploys production-grade machine learning and data engineering systems for regulated industries and high-growth companies.
We've shipped computer vision microservices for pharmaceutical manufacturing with FDA 21 CFR Part 11 compliance, achieving sub-100ms processing latency in on-prem Kubernetes clusters. We've built LLM-powered RAG pipelines for financial analytics automating research workflows with Pinecone vector search and MLflow observability. We've architected Azure data platforms for Caribbean government agencies, engineering Data Factory pipelines that extract on-prem SQL Server data into Synapse for Power BI analytics across MSME ecosystems.
Our stack centers on MLOps-first architecture: FastAPI microservices, Docker containerization, CI/CD with GitHub Actions, and comprehensive monitoring through Prometheus, Grafana, and MLflow. We deploy across AWS, Azure, and GCP with expertise spanning vector databases (Weaviate, Pinecone), LLM orchestration (LangChain, LlamaIndex), and distributed compute (Apache Airflow, PySpark).
Whether you need ONVIF-based PTZ camera control with MQTT messaging, retrieval-augmented generation with semantic search, or compliant data pipelines with audit trails—we architect systems that pass security reviews, handle edge cases, and run reliably at scale.
Meet the Founder
Dr. Jody-Ann S. Jones
Founder & Principal ML Engineer | AI Engineer | Data Engineer | AWS Certified Machine Learning – Specialty
Dr. Jody-Ann Jones is an AI engineer, machine learning engineer, and data engineer with 5+ years building and deploying production-grade ML pipelines, RAG platforms, and data automation systems across AWS, Azure, and GCP.
She founded The Data Sensei to help companies translate their data strategy into operational reality—from data ingestion and pipeline orchestration to ML model deployment, retraining, and observability. Her work spans AI engineering (LLM-powered systems, vector search, RAG pipelines), data engineering (ETL/ELT, real-time pipelines, cloud data platforms), and full-stack software engineering (FastAPI backends, React/Next.js frontends, mobile apps).
Her specialty lies in delivering scalable, production-ready systems using Docker, FastAPI, Airflow, and LangChain—paired with vector databases like Qdrant and Pinecone, and monitoring stacks like Prometheus + Grafana + MLflow.
As a technical educator and speaker, she's passionate about mentoring the next wave of AI engineers, ML engineers, and data leaders. She's taught Python for Data Analysis at the University of the Commonwealth Caribbean and published extensively on Medium covering MLOps, AI engineering, and production ML systems.
Her Ph.D. in International Political Economy (with quantitative focus on econometrics, statistics, and game theory) gives her a unique lens for understanding how AI and data systems drive business strategy.
Select Client Engagements
Pharmaceutical Manufacturing Client – Computer Vision & MLOps
- Deployed hybrid image capture and camera control microservice combining MQTT messaging with RESTful APIs for automated visual inspections
- Implemented ONVIF-based PTZ control and adaptive RTSP streaming (4K → 720p fallback) optimized for VPN conditions
- Deployed image capture service to an on-prem Kubernetes cluster with sub-100ms latency while maintaining FDA 21 CFR Part 11 audit trails
Financial Analytics Client – AI Engineering (RAG Platform)
- Architected LLM-powered RAG pipeline automating financial research with Pinecone vector search, AWS DocumentDB metadata storage, and LangSmith observability
- Delivered production-ready FastAPI backend with role-based authentication and continuous testing via pytest and GitHub Actions
Caribbean Export Development Agency – Data Engineering (IDB-Funded)
- Lead Data Engineer on Digital Maturity Dashboard initiative
- Engineered modular Azure Data Factory pipelines extracting on-prem SQL Server data into Azure Synapse, powering Power BI dashboards delivering insights across Caribbean MSME ecosystems
Entrepreneurial Ventures
Founder and CEO, BuyersMarket Inc. – Full-Stack Mobile Development
Jamaica's first grocery price intelligence app tracking 17,240+ products across 31 stores. Built production mobile app (Flutter, Firebase) with proprietary A-B-C price rating algorithm analyzing 90-day price history, crowdsourced verification system, and AI-powered route optimization. Launching April 2026 with 200-person beta program addressing Jamaica's 8.1% food inflation crisis where households spend up to 47% of income on groceries.
Technical Architect, UmaVoice – AI-Powered Mobile App
AI-augmented AAC app for non-verbal users. Designed full-stack architecture integrating ML-based predictive text and speech output using Flutter, TensorFlow Lite, Firebase, and Google Text-to-Speech API. Directed developer hiring, QA, and release management with accessibility-first, multilingual design.
Prior Experience
Data Scientist, Digicel Group
Built Digicel's first in-house churn prediction model, integrated outputs into Oracle Data Warehouse, and established feedback-driven retraining loop improving model robustness.
Data Steward, Munich Data Science Institute (Technical University of Munich)
Authored data management blueprints and governance frameworks improving research reproducibility across departments. Led cross-institutional initiatives standardizing metadata and optimizing lifecycle management.
Education & Certifications
Ph.D., International Political Economy (Quantitative Focus), Old Dominion University
Specialized in econometrics, statistics, and game theory
Certifications:
- AWS Certified Machine Learning – Specialty
- Le Wagon Data Engineering Bootcamp
- Udacity ML DevOps Engineer Nanodegree
Thought Leadership
Published author on Medium covering MLOps, AI engineering, production ML systems, and data engineering best practices
University lecturer at University of the Commonwealth Caribbean teaching Python for Data Analysis
Technical educator and speaker passionate about mentoring the next wave of AI engineers, ML engineers, and data leaders
Why Choose Us
What Sets Us Apart
We deliver production-grade AI, ML, and data systems that pass compliance audits, handle edge cases, and run reliably at scale—not proof-of-concepts that break under real-world load.
Production-First Engineering
We architect for production from commit one—not as an afterthought. Every system includes:
- Docker containerization and Kubernetes orchestration for scalability
- CI/CD pipelines with GitHub Actions for automated testing and deployment
- Comprehensive monitoring via Prometheus, Grafana, and MLflow
- Security & compliance built-in (FDA 21 CFR Part 11, GxP, role-based authentication)
Our systems are designed to pass security audits, meet regulatory requirements, and stay operational under production load.
AI & Data Engineering Expertise
We specialize in the technologies that matter in 2026:
- AI Engineering: LLM-powered RAG platforms, vector search (Qdrant, Pinecone), LangChain/LlamaIndex orchestration
- MLOps: FastAPI microservices, model versioning (DVC), experiment tracking (MLflow), automated retraining pipelines
- Data Engineering: Cloud data warehouse expertise (Azure Synapse, AWS Redshift, BigQuery), ETL/ELT with dbt and PySpark, workflow orchestration with Airflow, streaming data pipelines with Apache Kafka
- Full-Stack Development: Next.js/React frontends, FastAPI backends, Flutter mobile apps
We don't just implement—we architect systems your team can maintain and evolve.
Regulatory & Compliance Experience
We've shipped systems meeting:
- FDA 21 CFR Part 11 compliance for pharmaceutical manufacturing
- GxP requirements with comprehensive audit trails and metadata schemas
- Enterprise security standards with role-based authentication and encrypted data pipelines
Every engagement includes detailed documentation, deployment validation scripts, and compliance artifacts supporting your QA/QC processes.
Knowledge Transfer & Team Enablement
We don't just build and disappear. Every project includes:
- Technical documentation covering architecture, deployment, and maintenance procedures
- Post-deployment support to ensure smooth operations
You get production systems and the knowledge to run them.
Who We Serve
Industries & Companies
Production-grade AI and data systems for regulated industries and high-growth companies.
Regulated Industries (Pharmaceutical, Healthcare, Finance)
Your Challenge:
You need FDA-compliant systems, comprehensive audit trails, security reviews, validation documentation, and systems that pass regulatory scrutiny—not demos that break under compliance requirements.
We've Delivered:
- Pharmaceutical manufacturing computer vision platforms with FDA 21 CFR Part 11 compliance and GxP audit trails
- Financial analytics RAG systems with role-based authentication, encrypted data pipelines, and comprehensive observability
What You Get:
Production systems with metadata schemas, audit logging, deployment validation scripts, regulatory documentation, and training for your QA/QC teams.
Startups & Growth-Stage Companies (Seed to Series B)
Your Challenge:
You need production velocity without accumulating technical debt. Manual processes are slowing your team. Data sits in spreadsheets instead of powering decisions. You can't afford to rebuild systems in 6 months because they weren't architected to scale.
We've Delivered:
- Data pipelines processing millions of events daily
- Automated ML retraining workflows reducing manual model maintenance from weeks to hours
- Customer analytics platforms replacing manual reporting with self-service dashboards
What You Get:
Modular, cloud-native systems with CI/CD from day one—FastAPI microservices, Docker containerization, comprehensive observability, and automated testing infrastructure that scales with your business.
Enterprise Data & AI Teams
Your Challenge:
You're modernizing legacy infrastructure or building new AI capabilities while navigating complex integrations, security policies, compliance requirements, and organizational constraints. Your existing systems are fragile. Your data team spends more time fighting infrastructure than delivering results.
We've Delivered:
- On-prem to cloud migrations (SQL Server → Azure Synapse) with zero downtime
- MLOps pipelines integrating with existing Oracle Data Warehouses and enterprise systems
- Data platform modernization enabling self-service analytics across business units
What You Get:
Migration strategies, Azure Data Factory / AWS / GCP pipeline implementations, MLOps workflows your team can maintain, comprehensive documentation, and training ensuring long-term operational independence.
Work with us
Book a FREE 30-minute discovery call to discuss your project. No sales pitch, just honest technical advice on how we can help your business thrive.