AI, ML & Data Engineering that Ships to Production
End‑to‑end machine learning and analytics engineering—feature stores, pipelines, and monitoring built for impact. MLOps • Observability • Compliance‑ready
Deliver Real Business Lift—Not Just Models
We build reliable data pipelines and ML systems that increase revenue, reduce costs, and improve decision‑making. From data ingestion and feature engineering to training, evaluation, and monitoring, we deliver AI that holds up in production.
AI/ML Engineering Capabilities
ML Problem Framing & ROI Modeling
Define clear business objectives, establish success metrics, estimate ROI, and validate ML use cases before building.
Data Engineering & ELT Pipelines
Build batch and streaming data pipelines with quality gates, data lineage tracking, and automated validation using dbt, Airflow, and Kafka.
Model Development: Classical ML & Deep Learning
Develop forecasting, NLP, and computer vision models using scikit-learn, XGBoost, PyTorch, and TensorFlow with proper cross-validation.
Retrieval‑Augmented Generation (RAG) & Vector Search
Build RAG systems with LangChain, LlamaIndex, and vector databases (Pinecone, Weaviate, Redis) for knowledge assistants and semantic search.
Feature Stores & Experiment Tracking
Implement feature stores for reusable features, track experiments with MLflow and Weights & Biases, and maintain model registries.
MLOps Pipelines & Model Governance
Automate model training, validation, and deployment with CI/CD for ML, version control, approval workflows, and automated testing.
Monitoring for Drift, Bias & Performance
Track model performance degradation, data drift, prediction bias, and latency with Evidently, WhyLabs, and custom monitoring dashboards.
Privacy & Compliance
Design HIPAA and PII-safe data handling patterns, implement differential privacy, data anonymization, and audit trails for regulated industries.
A/B Testing & Controlled Rollouts
Deploy models with canary releases, shadow mode validation, and A/B testing frameworks to measure real-world impact safely.
AI/ML Tech Stack & Tools
Data Engineering
dbt — Data transformation and modeling.
Airflow — Workflow orchestration.
Spark & Kafka — Stream and batch processing.
Fivetran/Stitch — Data ingestion pipelines.
Storage & Warehousing
Snowflake & BigQuery — Cloud data warehouses.
PostgreSQL (pgvector) — Vector database for embeddings.
S3 / Azure Blob — Object storage for data lakes.
ML & Deep Learning
Python — Primary ML language.
scikit-learn & XGBoost — Classical ML algorithms.
PyTorch & TensorFlow — Deep learning frameworks.
LLM, RAG & MLOps
LangChain & LlamaIndex — LLM application frameworks.
Pinecone, Weaviate, Redis — Vector databases.
FastAPI, Triton, TorchServe — Model serving.
MLflow, Weights & Biases, Evidently — Experiment tracking and monitoring.
AI/ML Development Process
1. Use‑Case Discovery & Success Metrics
Define business targets, establish guardrails, validate data availability, and set clear ROI expectations before development.
2. Data Audit & Architecture
Assess data quality, establish lineage tracking, ensure compliance requirements, and design scalable data architecture.
3. Modeling & Evaluation
Build baseline models, engineer features, perform cross-validation, run ablation studies, and evaluate model performance against business metrics.
4. Deployment
Deploy models as batch jobs or real-time endpoints, implement canary or shadow deployments, and establish rollback procedures.
5. Monitoring & Feedback Loops
Monitor data drift, prediction bias, model performance SLAs, latency metrics, and establish automated alerting for degradation.
6. Iterate & Scale
Implement cost controls, establish retraining cadence, prioritize model improvements, and plan feature roadmap based on business impact.
Featured AI/ML Projects
AI/ML systems delivering measurable impact
Smart Mooring IoT Analytics
Real-time environmental data processing and analytics for marine IoT sensors. Machine learning for weather prediction, anomaly detection, and predictive maintenance of buoy systems.
Eye Handbook Medical Data Intelligence
Healthcare data management with ICD code intelligence, medical knowledge base, and PHI-compliant data storage for ophthalmology diagnostic tools and calculators.
Hydrosoft Predictive Analytics
Predictive analytics for water treatment operations. Time-series forecasting for service scheduling, route optimization algorithms, and equipment maintenance prediction models.
What Our Clients Say
“They delivered exactly what we needed on time.”
One Team US LLC developed a custom website for a construction company to showcase their projects and services.
Operations Director, Construction Company
Read MoreHire AI/ML Talent
Looking for specialized AI/ML expertise? Explore our hiring options for ML engineers, data engineers, and analytics engineers.
Hire ML Engineer
Build production ML models with PyTorch, TensorFlow, MLOps pipelines, feature engineering, and model monitoring.
Hire Data Engineer
Design scalable data pipelines with dbt, Airflow, Spark, Snowflake, and real-time streaming architectures.
Hire Analytics Engineer
Build BI dashboards, data models, and self-service analytics with dbt, Looker, Power BI, and data visualization.
Ready to Productionize AI?
Let's discuss how our Michigan-based AI/ML team can build data pipelines and models that deliver measurable business impact.