Vetted Amazon Kinesis Professionals

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

“Backend/ML systems engineer who built a Flask + PostgreSQL internal ticketing platform and demonstrates strong database/ORM performance depth (indexes, partitioning, RLS multi-tenancy). Notably optimized a high-throughput attachment OCR/embedding pipeline with batching, deduplication, and Redis caching, cutting median latency from 45s to 10s and reducing worker cost by 35% while increasing throughput 4x.”

“QA automation engineer who owned an end-to-end automated regression suite for a PlayStation digital store flow (login through checkout/payment), building a hybrid POM/data-driven framework from scratch with Selenium/TestNG/Cucumber and also using Playwright/TypeScript and Cypress. Integrated the suite into Jenkins CI/CD with nightly runs and reporting, improved coverage (happy + negative paths), and reduced release risk by catching critical issues like session timeout and transaction/payment defects before production.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”