Michael Raymond
@michaelraymond1
I build production agentic AI, RAG, and data platforms for regulated enterprises.
What I'm looking for
At Deloitte, I build agentic AI for a healthcare network, enabling engineers to query AWS, Azure, and GCP in plain English and cutting incident response time by 40%.
I’ve built hybrid RAG systems at Thomson Reuters for tax professionals, reducing factual errors by 35% through citation attribution, LLM evaluation, confidence calibration, and responsible AI guardrails. I also build predictive maintenance and anomaly-detection systems that surface degradation earlier and reduce unplanned outages.
Before AI products, I built real-time data platforms at WPP Media–Choreograph, processing billions of events daily for programmatic advertising, and HIPAA-compliant healthcare pipelines at Centene that saved 15+ hours per week in reconciliation work.
I build systems, teams, and trust across time zones—leading architecture, mentoring engineers, and documenting production practices through model cards, runbooks, and ADRs.
Experience
Work history, roles, and key accomplishments
Architected LangGraph multi-agent system for UHS, cutting incident response time by 40%. Built hybrid RAG pipeline reducing hallucinated answers by 60% and deployed predictive maintenance model reducing unplanned outages by 30%.
Designed and deployed hybrid RAG pipelines over IRS code and Checkpoint Catalyst, reducing factual errors by 35%. Developed LLM evaluation framework with Ragas and custom QA eval sets, and implemented guardrails and responsible AI filters.
Senior Data Engineer
WPP Media – Choreograph
Mar 2018 - Mar 2020 (2 years)
Architected PySpark/Kafka streaming pipelines on AWS EMR and built Airflow orchestrated ELT workflows, processing billions of events daily with sub-minute latency. Developed cross-source entity resolution and semantic matching models using Python and Databricks.
Built Python/SQL ETL pipelines with HIPAA-compliant PHI handling, RBAC, and full audit trails, and implemented data quality validation with Great Expectations, reducing reporting errors by 20%. Automated care management workflows using Airflow on AWS, saving 15+ hours/week.
Education
Degrees, certifications, and relevant coursework
The University of Texas at Austin
Bachelor of Science, Computer Science
2012 - 2016
Grade: 3.9 / 4.0
Activities and societies: Relevant Coursework: Machine Learning, Distributed Systems, Database Systems, Algorithms.
Bachelor of Science in Computer Science from The University of Texas at Austin, with a GPA of 3.9/4.0.
Tech stack
Software and tools used professionally
GitHub
GitLab
Kubernetes
GitHub Actions
GitLab CI
PySpark
DB
Django
Databricks
Neo4j
Redis
Terraform
Jira
PyTorch
MLflow
Kafka
FastAPI
Airflow
GuardRails
s3-lambda
SQL
Azure Cosmos DB
LangChain
LlamaIndex
Pydantic
Gremlin
Delta Lake
Great Expectations
Azure Container Apps
Ragas
Cosmos
Agentic
Faiss
LangGraph
LangSmith
Loops
Bridge
Factory
Availability
Location
Authorized to work in
Salary expectations
Social media
Job categories
Skills
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