Vetted Sentiment Analysis 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 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.”

“Built and shipped an AI-powered journaling app feature that analyzes user text for emotions in real time, owning everything from React/TypeScript UX (dynamic theming + Framer Motion) to a FastAPI backend integrating Hugging Face. Has hands-on production experience deploying Dockerized services on AWS (EC2/RDS/S3/CloudWatch) with GitHub Actions CI/CD, and resolved a real latency/scaling incident by converting blocking external API calls to async with retries/timeouts.”

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.”

“FAU-based analytics candidate with hands-on academic project experience across SQL data preparation, Python/NLP sentiment analysis, and predictive modeling. They stand out for turning messy datasets into clean reporting tables, building reproducible analysis workflows, and translating findings into practical recommendations around operations, credit risk, and marketing ROI.”

“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”

“Analytics and automation candidate with experience delivering data and AI solutions for major public-sector and enterprise clients including STC, Abu Dhabi Ports, and the Ministry of Culture. They combine SQL, Python OCR pipelines, dashboards, and cloud LLMs to turn messy unstructured data into scalable workflows, with reported impact including 70% faster document processing and 80-85% reduction in manual resume screening.”

“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”