Vetted Databricks Professionals

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“Backend/data platform engineer who owned a near-real-time patient feedback ingestion system, building a FastAPI + Kafka service with Snowflake/Airflow orchestration. Demonstrates strong production Kubernetes/GitOps practices on AWS EKS (Helm, Argo CD, Sealed Secrets) and solved real-time data integrity issues via idempotent processing with Redis.”

“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”

Sreevalli Santhavel

Screened

Director-level AI Product Manager specializing in GenAI, LLMs, and SaaS platforms

Texas, USA12y exp

“Technical Product/Program Manager with architect-level involvement who leads customer-facing product builds from sales discovery and Figma design through engineering estimation, schema decisions, and cloud deployment. Has shipped integrated ecommerce and auction products, including vehicle inventory workflows tied to Salesforce, Stripe, and QuickBooks, and has applied AI/ML to warehouse QA, defect detection, and pricing recommendations.”

“Built and deployed a production LLM-powered demand and churn forecasting system for an e-commerce client, combining open-source LLMs (LLaMA/Mistral) and Sentence-BERT embeddings to generate business-friendly explanations of forecast drivers. Strong focus on data quality and model trust (validation, baselines, segmented monitoring) and production reliability via Airflow-orchestrated pipelines with readiness checks, retries, and ongoing drift/A-B testing.”