Vetted Apache Spark Professionals

“Data engineer who has owned end-to-end ELT pipelines on Airflow + AWS (S3/Glue/Lambda) with Snowflake/Redshift, processing millions of records per day and tens of GBs via PySpark. Built strong data quality and reliability practices (40% quality improvement, 99%+ uptime), and also designed a resilient web-scraping system with anti-bot defenses and schema-change versioning plus REST APIs for serving curated data.”

Muhammad Murtaza Murtaza

Screened

Mid-level Machine Learning Engineer specializing in NLP, Computer Vision & Predictive Analytics

Islamabad, Pakistan5y exp

“Built a production LLM fine-tuning pipeline for domain-specific code generation at Pigeonbyte Technologies, including automated collection and rigorous quality filtering of 10M+ code samples (AST validation, sandbox execution/testing, deduplication, drift monitoring, and human-in-the-loop review). Also implemented end-to-end ML orchestration in Apache Airflow with data quality gates, dataset versioning in S3, benchmarking, and automated model promotion, and has a reliability-first approach to agent/workflow design.”

Akash Parekh

Screened

Mid-level Full-Stack Developer specializing in React, Node.js, and cloud deployment

Toronto, Canada3y exp

“Frontend engineer with experience building enterprise ERP/CRM dashboards in React + TypeScript, including cron-driven data updates and differentiated state flows. Emphasizes CI/CD pipeline setup, performance optimization (Core Web Vitals, React Profiler), and disciplined delivery practices (risk-based QA, phased rollouts); also has Figma-to-responsive implementation experience.”

“Full-stack engineer who built an end-to-end healthcare logistics application on a Microsoft services stack, delivering Spring Boot microservices and a responsive React frontend. Strong focus on production concerns—API performance tuning (indexing/caching/pagination) and microservice reliability (timeouts/retries/circuit breakers)—with Dockerized deployments and standardized Jenkins CI/CD.”

“QA/supplier-performance focused candidate who uses defect and delivery data to spot recurring issues early, identify root causes tied to rushed timelines/high workload, and implement practical process changes (e.g., added validation steps and tightened defect definitions). Emphasizes clear, metric-backed communication to align internal stakeholders and suppliers, then monitors post-change results to confirm sustained improvement.”

Sarang Pratham

Screened

Junior Software Engineer specializing in data engineering and GenAI

Kundapur, India3y exp

“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.”

“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”

Bishara Bishara

Screened

Entry-Level Software Engineer specializing in AI/ML pipelines

1y exp

“Built a production LLM-powered interview-prep app that ingests job postings and generates tailored preparation plans. Iterated from a single generalist LLM to a multi-LLM pipeline and used RAG to ground the final chat assistant on locally stored intermediate outputs; has also experimented with n8n vs Python-coded pipelines for orchestration.”