Vetted Natural Language Processing Professionals

“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.”

“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.”

KARISHMA PAPPU

Screened

Junior AI/ML Engineer specializing in machine learning and data pipelines

2y exp

“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.”

“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.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”

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.”

Muhammad Midhat

Screened

Senior Full-Stack/Backend Engineer specializing in APIs, distributed systems, and AI integrations

9y exp

“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”

“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.”

“Unity developer with hands-on experience evolving enemy AI from event-driven logic to FSMs and ultimately behavior trees, improving both performance and debuggability while making enemy reactions feel faster and more consistent to players. Has built multiple multiplayer games using Photon PUN 2 and Unity NetCode, focusing on server-authoritative synchronization and minimizing network traffic; also applies AI tools to accelerate prototyping and diagnose complex issues like OPC UA tag mismatches.”

“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”