Vetted Data Validation Professionals

Panita Vaishnavi Kanaguduru

Screened

Mid-level Business Analyst specializing in retention, churn, and revenue analytics

Remote4y exp

“Early-career data analyst with hands-on experience at SuperWorld building SQL and Python analytics pipelines for product and growth use cases. They stand out for turning messy event and transaction data into validated funnel datasets, automating reporting to cut manual effort by ~40%, and partnering with product and marketing teams on conversion and engagement metrics.”

“Backend-focused engineer with experience at Ramp, Easebuzz, and George Mason University, spanning data pipelines, workflow automation, and production reliability. Stands out for quantifiable performance gains, strong debugging instincts in distributed job systems, and translating ambiguous finance operations processes into measurable automation outcomes.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“Full-stack engineer who built a multi-tenant SaaS analytics dashboard end-to-end with Next.js App Router/TypeScript, emphasizing server components + React Query for performance and real-time UX. Demonstrated strong production ownership post-launch (observability, DB/query tuning, caching strategy) and has concrete wins like 30–40% load-time reduction and Postgres query latency cut to under 200ms.”

Venkata Sai Niranjan Nadipineni

Screened

Senior Salesforce Developer specializing in Lightning and enterprise CRM

New Jersey, USA8y exp

“Salesforce-focused candidate with hands-on ownership of end-to-end Service Cloud automation, including Apex schedulable jobs for inactive portal user tracking, warning notifications, deactivation, and reporting. Demonstrates strong architectural judgment across Apex, LWC, and Aura, with particular strength in scalable design, performance optimization, and rigorous testing/debugging in complex Salesforce environments.”

Abhishek Ghaisas

Screened

Intern-level Data Scientist specializing in AI and full-stack applications

Pune, India1y exp

“Engineer with hands-on experience building production ML and Python backend systems, including a real-time social media monitoring pipeline handling 1000+ events per second and a prototype AI operations assistant for Seattle-Tacoma Airport. Stands out for combining reliability engineering, automation, and LLM/NLP-to-SQL work, with measurable impact such as improving uptime from 92% to 99.4%.”

“Builder of practical AI-backed products across developer tooling, travel search, defense, and healthcare-style workflows. They shipped an MCP/FastAPI/Gemma context-compaction system that cut token usage by about 80%, built a flight-price AI layer that validates LLM output against live search data, and helped shape a visionOS command center for a military air wing.”

“Full-stack software engineer with hands-on experience shipping AI-driven product experiences, including a conversational travel planner and a RAG-based PDF question-answering system. Has also built enterprise automation APIs at Accenture for network diagnostics, combining backend engineering, testing automation, and user-focused product simplification for non-technical operations teams.”

Atharva Jagtap

Screened

Junior Full-Stack Software Engineer specializing in cloud and AI/ML applications

United States, Remote2y exp

“Full-stack engineer with hands-on experience across e-commerce personalization, enterprise RAG assistants, and cloud infrastructure automation. They’ve shipped AI features using Azure LLM APIs and vector search, improved recommendation engagement, and worked across frontend, backend, ML-informed analytics, and AWS infrastructure in early-stage environments.”

“Built and owned invoice automation and alerting products at Neuralix, automating multi-site electricity invoice ingestion from PDFs into validated JSON with strict schema enforcement and LLM-based validation (reported ~98% compliance). Delivered zero-manual processing at 200+ invoices/month and ~5x faster throughput, and designed a domain-driven alert lifecycle to reduce noisy notifications and improve operational response.”

“Backend Python engineer with end-to-end ownership of secure financial data systems integrating banking/credit/payment platforms, including automated ingestion and reconciliation of large financial statements. Built modular Dockerized Django REST services with pandas-driven validation/normalization and Postgres/Mongo persistence, and supported a phased migration from legacy VM services to AWS containers with stateless refactors and parallel-run integrity checks (run IDs/checksums). Works closely with platform teams on GitOps/CI readiness and deployment coordination (e.g., ArgoCD-managed sync policies).”

“Cloud/distributed-systems engineer who has shipped real-time, offline-capable ledger/expense infrastructure and solved tricky cross-layer production bugs (carrier handoff retries causing duplicate writes) using packet captures and device logs. Also built modular Python ETL/catalog pipelines for e-commerce with config-toggled plugins for customer-specific pricing/SKU rules, and iterated product changes directly with on-site fulfillment operators using feature flags.”

“QA professional with end-to-end ownership who combines automated, rule-based data validation (expected vs actual) with structured CAPA practices (5 Whys, Pareto, SOP updates, in-line checks, and hands-on training). Experienced coordinating multi-workstream QA timelines via centralized dashboards, weekly cadence, and escalations with third-party/global stakeholders.”

Tao Chen

Screened

Senior Full-Stack Engineer specializing in AI-powered web products

Mountain View, CA7y exp

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”

Aneri Patel

Screened

Junior Machine Learning Engineer specializing in LLM fine-tuning and semantic retrieval

Washington, D.C.2y exp

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

Kristoffer Kosunen

Screened

Mid-level UX/HCI & Game Designer specializing in VR and interactive experiences

7y exp

“Early-career UK-based game developer with extensive Unity experience and a postgraduate engineering project involving a Meta Quest prototype built in Godot. Built a solo first-person puzzle/exploration game with FromSoftware-inspired ambiguous narrative, and uses AI workflows for concept visualization and for validating/prototyping gameplay logic before implementing in C#.”

Tanisha Dalwadi

Screened

Mid-Level Full-Stack Product Engineer specializing in TypeScript and React

Arizona, USA4y exp

“Software engineer and co-founder with 0-to-1 SaaS experience who built and owned an end-to-end reporting/analytics dashboard on Next.js App Router + TypeScript, including Postgres schema design, aggregation query optimization, and post-launch performance/monitoring. Has delivered measurable React dashboard performance gains (~35% improvement in time-to-insight) and built durable, idempotent job/state-machine workflows using serverless functions and Postgres.”

“Data engineer who has owned high-volume production pipelines end-to-end (200–300 GB/day) on AWS, implementing strong data quality/observability and achieving 99.9% reliability while cutting data issues ~33%. Also built a large-scale external data collection system ingesting millions of records/day with anti-bot/rate-limit handling and backfill tooling, and shipped a versioned REST service exposing curated Snowflake data to downstream teams.”

“Built a production 13-step LLM agent pipeline at Sentari AI that converts diary entries into personalized AI responses and a persistent structured user profile. Stands out for a pragmatic approach to production AI: strict schemas, function calling, state-machine orchestration, confidence thresholds, logging, and validation layers to control non-determinism and prevent bad profile updates.”

Yanet Fernandez

Screened

Junior Data Analyst specializing in healthcare analytics

Fort Myers, FL2y exp

“Analytics/data professional with hands-on experience turning messy semi-structured CRM JSON data in Snowflake into clean reporting layers using SQL and validation logic. Brings a practical mix of data engineering, Python automation, metric design, and stakeholder alignment to improve reporting accuracy and speed of decision-making.”

“Engineer with hands-on experience building adaptive assessment and LMS-style platforms across React/TypeScript, edge/serverless backends, and Postgres, with strong evidence of cross-layer debugging and performance optimization. Also brings ML product experience from a small-team internship, where they shipped a CatBoost-powered investor demo under ambiguity and created reusable inference infrastructure.”

“Current data analyst intern at Across and MBA student who combines SQL, Python, and visualization skills across both workplace and academic analytics projects. Has hands-on experience cleaning messy customer service and time-tracking data for payroll-related analysis, plus building sentiment and trend-based analyses in Python and dashboards for stakeholder communication.”

“Analytics professional with hands-on experience turning messy CRM and campaign data into standardized reporting datasets using SQL and Python. At EcoSolutions/EcoServices, they built reusable KPI pipelines, dashboards, and metric definitions that reduced manual reporting and improved time to conversion by roughly 55% through better segmentation and campaign optimization.”

“Analytics-focused candidate with hands-on experience building compliance and contract utilization reporting from messy contract, vendor, subcontractor, and payment data. They combine SQL and Python automation to improve reporting speed and accuracy, and show strong stakeholder discipline through validation sessions, documentation, and dashboard adoption.”