Steven Pate
I’m Steven Pate, a Senior Software Engineer and Data & AI specialist with 10+ years of experience building cloud-native Python backend systems and large-scale data platforms.
About
I’m Steven Pate, a Senior Software Engineer and Data & AI specialist with 10+ years of experience building cloud-native Python backend systems and large-scale data platforms. I focus on ETL/ELT, distributed architectures, and intelligent pipeline automation that turn structured and unstructured data into reliable analytics and operational workflows.
I also design and ship LLM-integrated systems—such as orchestration frameworks, agent-based architectures, semantic models, metadata/knowledge-graph approaches, and MLOps/LLMOps monitoring—to improve discovery, reduce manual transformation effort, and support responsible AI practices. I enjoy partnering with data architects and governance stakeholders to deliver secure, observable solutions that perform at scale across AWS, Azure, and GCP.
- 9 years' experience
- Joined October 2026
Industry experience
- Financial Services
- Software & Internet
- Professional Services
Work experience
Led a team to build AI-powered data pipelines and intelligent automation for a SaaS platform, integrating LLM orchestration and agent-based architectures for natural-language query support, metadata enrichment, and schema understanding across structured and unstructured sources. Built and maintained Python microservices and RESTful APIs generating star-schema and integration workflows that processed 2M+ records daily with high availability and fault tolerance on AWS and GCP. Designed reusable semantic models and metadata frameworks aligned with governance needs, improving observability, security, testing, and CI/CD practices and reducing production incidents while accelerating release cycles. Implemented Spark-based Lakehouse data discovery/transformation workflows containerized with Docker/Kubernetes, enabling LLM integration into analytics platforms and mentoring engineers through code reviews.
Developed scalable Python microservices and backend APIs for a FinTech lending platform, implementing ETL/ELT and integration workflows for transaction processing, credit scoring, and risk analytics with security compliance, audit trails, and regulated-data protections. Designed metadata-driven architectures and semantic models to automate schema mapping and enable LLM-based natural language query for business analysts, reducing data access time for non-technical users. Implemented MLOps/LLMOps workflows for evaluation, AI observability, and responsible AI practices with CI/CD and Docker-based deployments on AWS and Azure. Built observability using Prometheus/Grafana and distributed tracing, and used Apache Airflow plus custom agent-based orchestration for transformation, inference, and retraining workflows with data lineage and governance compliance.
Built enterprise insurance platforms for property & casualty carriers using Python microservices and data integration workflows on AWS and GCP, supporting policy, billing, claims, and digital insurance processes. Implemented metadata enrichment and schema understanding to automate transformations and reduce manual reconciliation across carrier systems. Integrated LLM-based document classification and natural language query capabilities for claims adjusters and underwriters, following performance, security, observability, testing, and deployment best practices. Established containerized deployment patterns with Docker/Kubernetes and CI/CD automation, and contributed to architectural decisions supporting AI-enabled analytics, search, and recommendation capabilities.
Education
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