Vetted Data Pipelines Professionals

“Built and shipped a production "Campaign AI" multi-agent system (LangGraph) that personalizes B2B outbound emails at scale using Apollo.io prospect data, clustering-based segmentation, and 21 persona variants. Notably uncovered that high click rates were largely email security scanners and created a validated bot-detection/scoring pipeline (timestamps/IP/user-agent/click patterns), bringing reported engagement down from ~40% to a trusted 5–8% that aligned with real conversions.”

“Built and scaled an internal AI code-search/assistant agent that expanded from engineering-only to broader internal users, tackling legacy code and inconsistent standards to make a RAG pipeline production-ready. Uses a metrics-driven approach (user feedback + automated Python evaluation for retrieval relevance and latency) and has handled high-pressure outages, including moving parts of the stack off AWS and adopting Milvus on internal infrastructure for resilience.”

“Built and deployed a production bilingual (Bengali/English) AI virtual assistant that replaced IVR for telecom customer service at massive scale (~15M users), integrating ASR/TTS, Rasa dialogue management, and custom NLP. Overcame low-resource Bengali data and noisy call-center audio with synthetic data augmentation and transformer fine-tuning, achieving significant production gains including ~50% reduction in support calls.”

“Hands-on technical/product-facing engineer with experience operationalizing LLM/agentic workflows and debugging them in real time using logs, traces, and metrics. Also supports go-to-market by running developer-focused live coding demos and partnering with sales on discovery and integration/maintainability concerns through clear technical documentation.”

“Built and productionized an LLM-based system that summarizes large volumes of unstructured content (customer feedback/internal docs) to reduce manual analysis and surface decision-ready insights. Brings strong reliability practices—prompt/schema constraints, validation checks, orchestration with Airflow/Databricks, and rigorous component + end-to-end testing—plus experience partnering closely with business stakeholders to drive adoption.”

“Unity VR developer with ~4 years of experience who has shipped an app to the Meta Quest Store. Notably worked on a challenging VR project integrating live point cloud data, focusing on bandwidth/latency, optimization, and compression to keep performance smooth in-headset.”

“Customer-facing engineer from Popular Tech who built and deployed tailored AI/automation features for enterprise voice systems. Experienced in integrating customer workflows via APIs, handling live production latency incidents through log tracing and rapid stabilization, and validating solutions through phased rollouts, monitoring, and direct on-site collaboration with clients.”

“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”

“QA/automation tester with experience across Cypress/Playwright/Cucumber and API testing who helped a large team with limited QA bandwidth by automating a previously manual Excel verification workflow using pandas. Reported a critical data-leakage defect and found ~40 issues during an old-to-new UI migration, collaborating regularly with developers/designers to improve product quality.”

“New-grad software engineer who built a real-world smart-city traffic camera/operations dashboard pitched to Peachtree Corners, GA, integrating Bosch cameras with an MQTT/Node-RED/InfluxDB pipeline and Grafana visualizations. Implemented rule-based alerts (including SMS dispatch for janitorial cleaning) and improved production reliability with queuing, redundancy, monitoring, and load testing, reaching ~99% message delivery. Actively seeking onsite, customer-facing roles with travel.”

“Early-career full-stack engineer who has not yet shipped a professional customer-facing product but has built sophisticated AI-driven systems in personal/academic work, including a multi-tenant AI knowledge base (async ingestion, pgvector semantic search, SSE real-time updates, knowledge graphs) and an AI-powered code review assistant designed to process thousands of jobs via Redis/BullMQ.”