Vetted Natural Language Processing Professionals

“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”

Alishba Ali

Screened ReferencesStrong rec.

Senior Full-Stack Software Developer specializing in React/Node and cloud-native apps

GTA, Canada7y exp

“Frontend engineer who launched Emoot (app.emoot.io) end-to-end and recently shipped the "My Savings Goal" feature from UI through production. Emphasizes scalable React + TypeScript architecture (Storybook-driven component library, strict typing, performance profiling/memoization) and pragmatic delivery in tight timelines by reusing existing components/logic and iterating screen-by-screen.”

“Full-stack AI engineer who has built and deployed multiple end-to-end LLM products, including an AI interview assistant, a multi-agent market research platform, and a policy document explainer. Particularly strong in productionizing agentic workflows, integrating tools like Whisper, Tavus, LiveKit, CrewAI, and LangGraph, and hardening messy real-world AI/document pipelines with validation, memory isolation, and fallback handling.”

Harshit Vashisth

Screened

Junior AI Engineer specializing in LLMs, RAG systems, and MLOps

Remote, United States1y exp

“Robotics software engineer who built an end-to-end system ("justmatrix"), focusing on multi-agent orchestration and a multi-RAG retrieval backend/API. Has hands-on ROS experience, including a custom node for reliable high-frequency sensor data routing, plus deployment automation using Docker, Kubernetes, and CI/CD.”

Jaiden Kettleson

Screened ReferencesStrong rec.

Entry-Level Full-Stack & AI Engineer specializing in chatbots and web apps

1y exp

“Data Science honors graduate (Maryville University) who has built Python/SQL backends and a capstone website handling sensitive user data. Emphasizes secure data handling (password encryption, secure database updates) and uses Git/GitHub Pages with CI/CD-style practices for managing and deploying changes.”

“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.”

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.”

“Built production-style backend and AI systems across internship and project work, including a real-time sports platform backend and a Smart Email Assistant using GPT-4. Stands out for combining classic backend performance engineering with practical LLM workflow design, including measurable latency improvements, high uptime, and debugging of non-deterministic model behavior.”

Toufiq Hasan

Screened

Entry-level Software Engineer specializing in backend and AI integration

New York, NY1y exp

“Built and shipped an LLM-powered crypto price tracking and alert agent in a web environment that scaled to roughly 10,000 users. The candidate emphasizes production reliability, structured outputs, and data normalization, and cites strong product impact including retention growth from 35% to 65% and roughly 95% positive user validation on alert interpretations.”

“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.”

“Recent graduate who independently built scholar.ai from scratch in roughly two weeks, shipping a full multimodal ingestion, retrieval, and grounded Q&A system for students. Also created Anchor SDK, an open-source framework for runtime hallucination detection and recovery, showing unusually strong depth in production LLM systems, evals, and reliability for an early-career candidate.”

“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.”