Vetted Web Scraping Professionals

Manas Agarwal

Screened

Junior Full-Stack Software Engineer specializing in Python APIs, React, and cloud AI integrations

Superior, CO2y exp

“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”

Abhinav Malkoochi

Screened

Junior Full-Stack Engineer specializing in AI and automation

Dallas, TX1y exp

“Startup-focused builder who created and iterated an MVP for Enky, a two-sided marketplace connecting music artists and creators, informed by hundreds of customer interviews. Implemented CI/CD, monitoring (PostHog/Sentry), and a complex payout pipeline involving scraping social platforms and routing escrow payments via Stripe, and has a track record of quickly debugging production issues (e.g., iOS-specific OAuth cookie failures).”

Himanshu Shivkumar Garg

Screened

Senior Full-Stack Java Developer specializing in Healthcare IT and FinTech

Florida, USA4y exp

“Built and owned an end-to-end HIPAA-compliant Next.js/TypeScript portal at Casedok using a microservices, event-driven architecture with Temporal-orchestrated SAGA workflows. Emphasizes production-grade quality (strict typing, Jest integration tests, CI + mandatory PR reviews) and operational reliability/observability (circuit breakers, idempotency, Prometheus alerts), plus experience designing external-facing APIs with Swagger, JWT, and backward-compatible versioning.”

“Backend/data engineer who has owned end-to-end production pipelines and data services, processing ~500K–1M records/day from APIs/logs into MySQL and serving via REST APIs. Strong focus on reliability and data quality (ELK + structured logging/monitoring), with measurable improvements (~30% reduction in bad data, ~20% query performance gains) and experience operating external data collection/scraping systems with anti-bot and schema-change resilience.”

Umair Azam

Screened

Senior Full-Stack Software Engineer specializing in Ruby on Rails and React

Lahore, Pakistan10y exp

“Frontend engineer with React/TypeScript experience who has led end-to-end UI work on a fintech product (React frontend with NestJS APIs), emphasizing performance (virtualized rendering, memoization, lazy loading, profiling) and quality at scale (unit tests and TDD). Also built and iterated on a snooker sports app, simplifying UX through audience-focused design decisions and streamlined onboarding.”

MANOGNA VADLAMUDI

Screened

Junior AI Engineer specializing in LLM evaluation, prompt engineering, and AI orchestration

Chicago, IL1y exp

“LLM workflow builder who has deployed a personalized GPT experience (including Delphi AI-based knowledge ingestion) and built a LangChain/LangGraph job-aggregation pipeline that ingests, normalizes/dedupes, filters, then uses an LLM to rank and summarize matches. Emphasizes production reliability with structured outputs, retries/fallbacks, metric-driven evaluation, logging/prompt versioning, and A/B testing, and collaborates with non-technical stakeholders through demo-driven iteration.”

Gomathy Selvamuthiah

Screened

Junior Data/AI Engineer specializing in MLOps, real-time pipelines, and LLM applications

Portland, US2y exp

“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”

Karan Baid

Screened

Intern Machine Learning Engineer specializing in Generative AI and RAG systems

Jaipur, India

“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”

“Built and productionized an LLM-powered customer support system using a RAG architecture with structured document ingestion, embedding retrieval, and prompt templates for product-specific grounding. Experienced diagnosing live agent/workflow failures (e.g., retrieval regressions after new docs) by refactoring ingestion/chunking and adding grounding constraints plus evaluation benchmarks. Also supports go-to-market by joining discovery calls, shaping MVP workflows into demos/prototypes, and creating post-launch documentation to drive adoption.”

Ajay Kudapa

Screened

Mid-level Software Engineer specializing in Healthcare IT and Web3

4y exp

“Software engineer with experience spanning healthcare middleware (patient records + insurance integration) and an AI fantasy football product built with React/TypeScript, Firebase, API gateways, and pandas-based data pipelines. Has hands-on microservices scaling experience (latency mitigation, async migration, state-based redesign) and built an internal feature-toggle dashboard that improved demo efficiency and sales outcomes.”

Dhruv Bansal

Screened

Intern Software Engineer specializing in backend systems and distributed data pipelines

San Francisco, CA1y exp

“LLM engineer with production experience building end-to-end document processing workflows that unify layout analysis, OCR, and downstream LLM reasoning. Has implemented reliability features (retries, robust error handling, OpenTelemetry logging) and built agentic systems using LangChain/CrewAI, including a student research-paper assistant, while collaborating closely with PMs and non-technical end users to reduce technical debt and simplify architectures.”

“Robotics engineer focused on simulation, modeling, and control, with hands-on sim-to-real experience from a soft, foldable “grasshopper” robot where friction/contact physics and servo dynamics drove real-world performance gaps. Built a ROS 2 voice-operated TurtleBot system integrating YOLOv5 + stereo depth for object picking with an attached arm, and debugged AMCL/SLAM to cut localization error from 10–13 cm to ~5 cm. Currently developing a quadruped in MuJoCo with a 3-layer control stack (RL + MPC + PD) and an RL training pipeline in JAX ahead of hardware.”

“Built and deployed production LLM applications including a natural-language-to-read-only-SQL system focused on ambiguity handling and query safety (schema whitelisting, intent validation, confidence checks, deterministic execution). Experienced with LangChain-based, modular agent orchestration and RAG document QA for large PDFs, with a metrics-driven testing/evaluation approach and cross-functional delivery with marketing on an AI content recommendation/search tool.”

Riya Stanly

Screened

Intern Data Scientist specializing in machine learning, NLP, and LLM fine-tuning

2y exp

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”

Kathan Patel

Screened

Mid-level Generative AI Developer specializing in Python and LLM applications

5y exp

“Currently working on Kavia AI, an end-to-end AI coding platform that lets users generate enterprise applications from prompts and existing codebases via SCM integrations. The candidate has hands-on experience across the GenAI stack—prompt engineering, LangGraph-based multi-agent orchestration, RAG, knowledge graphs, FastAPI, and AWS monitoring—with a focus on making software creation accessible to non-technical users.”