Vetted Data Validation Professionals

“Backend Python engineer who owned an AI-driven healthcare staffing matching service, rebuilding the model inference/data pipeline to eliminate blocking bottlenecks and cutting API latency by ~33%. Experienced running Python services on Kubernetes with GitOps/ArgoCD, and has executed a cloud-to-on-prem rollout under tight resource and tooling constraints while also building event-driven streaming updates via a message broker.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“QA automation engineer who owned an end-to-end test suite for a financial payments application, building cross-layer E2E coverage (UI/API/DB) and integrating it into CI with smoke-on-commit and nightly regression. Caught high-impact issues including duplicate payments caused by missing idempotency in backend retry logic and an RBAC authorization gap, and has hands-on experience stabilizing flaky Cypress tests via network-call synchronization.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

Rupak Chand

Screened

Junior ML Data Associate specializing in AI training data and LLM prompt evaluation

Connecticut2y exp

“Applied ML/embodied AI practitioner who built an on-device gesture-control system for smart-home lights using Raspberry Pi + camera, focusing on privacy-preserving real-time inference and hardware-constrained optimization (async pipeline + TF Lite INT8). Also made a high-impact architecture decision for an ML content evaluation/QA pipeline processing millions of annotated text samples weekly, reducing batch runtime from ~6 hours to ~40 minutes while lowering compute cost.”

Abhishek Soni

Screened

Mid-level Full-Stack Developer specializing in React and scalable web applications

Mumbai, India3y exp

“Backend/data engineer with hands-on production experience across FastAPI microservices and AWS data platforms. Has delivered serverless and Glue/EMR-based ETL pipelines with strong observability (Prometheus/Grafana/Sentry, CloudWatch/SNS), schema-evolution resilience, and measurable SQL performance wins (5 min to <30 sec). Open to onsite meetings in the Bethesda, MD area and flexible on remote arrangements.”

Prasanth Sai

Screened

Mid-level Data Engineer specializing in cloud lakehouse/warehouse pipelines

4y exp

“Data engineer with HCA Healthcare experience building and operating end-to-end AWS-based pipelines for clinical and operational reporting (50–100 GB/day), serving curated data into Redshift/Snowflake for Power BI/Tableau. Emphasizes production reliability (Airflow SLAs/retries/alerting, logging/observability) and strong data quality controls (reconciliations, schema/null/duplicate checks), and has shipped versioned REST APIs to expose warehouse data to downstream systems.”

“Accenture data engineer who greenfielded a supply-chain lakehouse platform, building an end-to-end medallion/Delta pipeline ingesting ~1.4TB/day from 17+ ERP/WMS/TMS/shipment sources. Delivered Gold datasets to Redshift/Synapse/Databricks SQL powering Power BI/Tableau with a 99.5% SLA, while cutting runtime 30% and cloud costs 16% through Spark/Delta optimizations and robust data quality controls.”

Ashrita Mishra

Screened

Mid-level Data Analyst specializing in analytics, ETL, and cloud data platforms

Jersey City, NJ4y exp

“Data analyst with 4 years of experience spanning banking and retail/marketing analytics. Has hands-on experience building churn analytics pipelines in SQL and Python, optimizing large-query performance, and turning stakeholder-aligned metrics into recurring dashboards and business actions.”

“Analytics professional with Intuit experience spanning modern data stack work, behavioral segmentation, and applied AI. They built dbt/Snowflake pipelines powering retention and churn dashboards, automated feedback classification with OpenAI/LangChain, and partnered closely with product and marketing teams to turn analytics into onboarding, targeting, and lifecycle messaging decisions.”

“Analytics professional with hands-on experience building SQL/Python pipelines, customer ID mapping logic, and self-serve BI dashboards across marketing/CRM and regulated aviation reporting environments. Particularly strong in turning messy multi-source data into trusted reporting assets, with repeated claims of major efficiency gains, faster decision-making, and high-confidence stakeholder adoption.”

Mehar Matta

Screened

Junior Marketing Analytics professional specializing in B2B SaaS and AI-driven insights

Richardson, TX2y exp

“Analytics professional with hands-on experience building SQL and Python workflows for marketing, funnel, and revenue reporting across tools like Salesforce, Google Analytics, and Power BI. They stand out for turning messy, multi-source data into trusted reporting layers, aligning sales and marketing on shared KPI definitions, and using segmentation/cohort analysis to improve campaign targeting and conversion performance.”

“Built from scratch a social media analytics MVP featuring an LLM-powered semantic search agent that became a core part of the product experience within a 6-week deadline. Stands out for focusing on production readiness early—retrieval-first design, explicit tool constraints, structured outputs, idempotent services, and practical eval/monitoring loops rather than demo-only AI.”

“Full-stack engineer with hands-on experience building real-time operational products across banking, insurance, and startup e-commerce environments. They’ve owned features end-to-end—from React/TypeScript dashboards and Redux performance tuning to Spring Boot, Kafka, AWS Lambda, and production monitoring—and have also shipped 0→1 capabilities where business impact was immediate, such as reducing overselling through inventory visibility.”

Lokesh Jain

Screened

Senior AI/ML Engineer specializing in supply chain and healthcare systems

Bentonville, AR6y exp

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“Game-studio QA tester with full lifecycle experience (feature kickoff through release/live updates), strong in structured test planning, edge-case discovery, and disciplined defect tracking. Partners closely with engineers via Jira/standups, providing detailed repro steps, logs, and evidence, and builds regression coverage to prevent recurring UI/data-loss issues.”