Vetted Data Validation Professionals

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

“Founding engineer with strong end-to-end product delivery across IoT + mobile + serverless cloud: built firmware for a Bluetooth-connected device (ESP32), a native Swift iOS app, and an AWS serverless backend (API Gateway/Lambda/SQS/SNS/DynamoDB) including payments via Stripe. Also shipped a separate startup product in 6 months: a React visual tool that generated HTTP/REST APIs with a Django backend, admin panel, and a code-generating CLI.”

prashanth Jamalapurapu

Screened

Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems

5y exp

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”

Sam Fish

Screened

Senior Full-Stack Engineer specializing in web platforms, cloud infrastructure, and data systems

Culver City, CA5y exp

“Full-stack/product-leaning engineer who owned an end-to-end AI Tutor feature (Claude-powered) shipped simultaneously to iOS/Android/web via Expo, with Cloudflare Workers backend and PostHog analytics. Built the company’s GitHub-based CI/CD to coordinate app store releases with backend blue/green deployments. Also has significant data engineering experience (including ~8TB/day workloads) using dbt/Fivetran plus sharding and hashing-based diffing for correctness.”

Ashritha G

Screened

Mid-Level Software Development Engineer specializing in distributed systems and cloud microservices

USA3y exp

“Software engineer with enterprise, customer-facing delivery experience across Outlier AI and Wipro—builds and productionizes workflow and integration solutions with a strong focus on real-world performance and reliability. Delivered a Firestore/Redis-backed real-time pipeline that cut page load times by 20% and held consistent performance across 10,000+ sessions, and has hands-on production incident experience stabilizing high-traffic microservices via caching, indexing, and safe canary deployments.”

Thanmayi Ravuri

Screened

Entry Software Engineer specializing in cloud backend and microservices

Tempe, AZ1y exp

“Built production-oriented LLM agent systems for incident investigation and CRM workflows using LangGraph, FastAPI, AWS, and retrieval grounding. Stands out for treating agents like real software systems—adding schema enforcement, retries, fallbacks, monitoring, and eval loops—and tying that work to measurable gains in accuracy, latency, and analysis speed.”

Pranav Zagade

Screened

Junior Software Engineer specializing in full-stack and AI systems

Arizona, USA3y exp

“Built and shipped an LLM-powered support agent for Community Dreams Foundation that automated intake and Jira ticket creation using RAG, structured outputs, and strong production guardrails. Demonstrated practical production AI experience with 92% routing accuracy, 98% uptime, and a 31% improvement in first-response accuracy, plus hands-on work in observability, evals, idempotency, and failure handling.”

Kunal Sanghvi

Screened

Mid-level Data Engineer specializing in AI, NLP, and LLM systems

USA3y exp

“Built and deployed a production AI customer support chatbot at Unique Design Inc. using FastAPI, AWS, Docker, and retrieval-based grounding on internal documents. Stands out for hands-on ownership across discovery, deployment, incident debugging, and post-launch iteration, with a strong focus on making LLM systems reliable and safe in real business workflows.”

Sowmya Mogireddy

Screened

Mid-level Data Analyst specializing in analytics, BI, and predictive modeling

CT, USA6y exp

“Analytics professional with cross-domain experience spanning healthcare claims, logistics optimization, and customer booking funnels. They combine strong SQL/Python execution with stakeholder alignment and operational adoption, and can point to measurable impact including 18% healthcare cost optimization and 24% logistics savings.”

“Marketing analytics candidate who combines strong SQL data engineering with Python automation to turn messy GA4, Instagram, and Postgres data into reliable reporting and decision tools. They’ve built cohort- and retention-based measurement frameworks that shifted teams away from vanity metrics, improved campaign allocation, and drove roughly 30% better two-week retention.”

“Analytics candidate who has built full-stack data products end to end: from cleaning 1M+ retail transactions in MySQL to Python-based market basket analysis, churn segmentation, and Streamlit dashboards with Gemini-powered business recommendations. Also built a reproducible predictive modeling workflow for concrete strength forecasting, showing a blend of SQL, machine learning, and stakeholder-friendly delivery.”

“ML engineer with hands-on experience building banking AI systems end-to-end, including a customer-targeting model that improved campaign response rates by about 10%. Also shipped a RAG-based banking FAQ/support feature with safety guardrails and production optimizations around retrieval quality, latency, and cost, plus reusable Python services that reduced duplicate work for other engineers.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend engineer with research and industry experience building data-intensive systems for healthcare and IoT. Built Python/Flask/FastAPI services with real-time ingestion and ETL into relational databases, emphasizing data quality, performance tuning, and secure access controls (JWT, RBAC, row-level filtering). Notably caught hardware-driven sensor anomalies others missed and implemented quarantine/alerting to prevent bad data from corrupting analytics.”

“Backend/AI engineer with internship experience building an AI-powered financial insights platform (FastAPI, Redis, BigQuery) and prior HCL experience leading a monolith-to-microservices refactor (Flask, Kafka) using blue-green deployments. Demonstrates strong performance/security focus (OAuth/JWT/RBAC, encryption) and measurable impact on latency, downtime, and ML model reliability; MVP was submitted to Google’s accelerator program.”

“Paid media performance marketer managing $50K+/month spend across Meta and Google for eCommerce and lead-gen, with a strong creative-testing orientation (UGC/video vs static) that produced ~25–30% lower CPA and ~35% higher ROAS when scaled. Builds full-funnel systems across Meta/TikTok (demand gen) and Google Search/PMax (high-intent capture), using marginal ROAS/CPA, frequency-based fatigue signals, and statistically grounded testing to scale or cut campaigns.”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.”

“Backend/cloud engineer with hands-on ownership of a climate data extraction pipeline (BeautifulSoup + Pandas ETL + CRON) that automated 50k+ monthly data points and removed ~20 hours/week of manual work. Also built a multi-AZ Kubernetes deployment for a Node.js system using Terraform and GitHub Actions (blue-green, rollbacks) and has Kafka/FastAPI experience from a healthcare plan management project.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“Internship experience at Paycom owning an end-to-end personalized course recommendation feature for an LMS, spanning SQL-based data pipelines, ML integration, and FastAPI REST services for real-time recommendations. Focused on production tradeoffs (latency vs. accuracy), scaling/SQL optimization, and post-launch iteration driven by engagement metrics.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”