Vetted Data Validation Professionals

Emily Hottal

Screened ReferencesStrong rec.

Director-level Product Leader specializing in AI, data, and customer engagement SaaS

Stow, MA17y exp

“Senior product leader at Airship driving both major platform modernization and the company's AI transformation. They led a contact-centric rebuild touching nearly the entire customer engagement platform, then built an AI recommendations product on Vertex/Gemini with a design-partner rollout model. They also bring rare depth across analytics, experimentation, personalization, and email marketing, plus experience rebuilding PM teams and shaping product strategy with executive leadership.”

Deeksha V

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud data platforms and FinTech payments

Atlanta, GA4y exp

“Backend engineer focused on financial systems, having evolved payments and reconciliation platforms using Python/FastAPI and PostgreSQL with an emphasis on idempotency, validation, and consistency. Has led monolith-to-services migrations using feature flags and shadow traffic, and implements defense-in-depth security (OAuth2/JWT plus DB-level row security) for multi-tenant environments.”

“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”

Mounika Nadendla

Screened ReferencesStrong rec.

Senior Data Engineer specializing in cloud data platforms and real-time streaming

5y exp

“Data engineer focused on building reliable, production-grade data systems end-to-end: batch and real-time pipelines (Airflow/Kafka/Spark) with strong data quality, monitoring/alerting, and incident response. Has experience integrating external API/web data with retries, throttling, and schema-change handling, and serving curated datasets to analytics (Power BI) and backend consumers with performance optimizations like Redis caching.”

Aldric Pinto

Screened ReferencesStrong rec.

Mid-level AI product and data lead specializing in analytics and healthcare AI

New Haven, CT4y exp

“Product-minded software engineering lead with a blend of backend, data engineering, cloud observability, and AI product experience. They’ve owned systems end-to-end, from ETL job builders that cut setup time 70% to hybrid-cloud observability workflows that reduced monitoring effort 80%, and also drove an AI marketing feature that improved conversion from 2% to 6%.”

“Robotics/remote-sensing software engineer who built and validated multisensor image-processing and spectral chemical-detection pipelines (RX anomaly detection, ACE), including calibration protocols with a motorized shutter and rigorous data QC. Uses white-box NumPy simulators to debug SLAM/registration issues before translating logic to C++, and partnered with hardware teams to solve temperature-driven signal variation via combined software calibration and improved thermal management.”

Fatema Tuzzohra

Screened

Senior Full-Stack Java Developer specializing in banking and healthcare platforms

Hicksville, NY12y exp

“Enterprise-focused full-stack/backend engineer with hands-on experience shipping a production LLM-powered document analysis platform for internal operations teams. They combine cloud-native microservices expertise across AWS/Azure with practical agent-system design, including async orchestration, model routing, observability, and safeguards for hallucinations, failures, and human-review fallbacks.”

“Backend-focused engineer with healthcare and finance experience (Cardinal Health, JPMorgan) who has owned end-to-end data flows powering dashboards, emphasizing strong validation/data quality and measurable frontend performance gains. Has shipped Spring Boot REST APIs with versioning and Swagger docs, and has stood up an MVP task management system with GitHub Actions CI/CD, Docker, and AWS EC2 deployment.”

Vaishnavi Kashyap

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in web platforms and sustainability analytics

1y exp

“Full-stack/backend engineer who owned a production digital assembly planning platform at Saint Gobain end-to-end (React/Node/Postgres), maintaining 99.9% uptime across 5 factory sites and driving a reported 90% improvement in factory-floor coordination. Also built and operated BigQuery + Vertex AI (ARIMA) forecasting/data pipelines processing 1M+ datapoints daily, with strong emphasis on idempotency and data-quality validation to prevent incorrect outputs.”

“Built and deployed an AI Copilot for Healthful Telehealth that helps dietitians generate personalized meal plans using patient data and real-time clinical context. Stands out for owning the full lifecycle—from workflow discovery and ETL/RAG architecture to production incident response and post-launch stabilization—while delivering roughly 30% gains in retrieval accuracy and latency.”

“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”

“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”

“Built and launched an internal AI troubleshooting assistant focused on safe, retrieval-first root cause analysis for enterprise systems, with strong attention to monitoring, fallback behavior, and post-launch iteration. Also owns full-stack product work across React and Java/Spring Boot, including high-volume financial operations workflows, and reports measurable LLM improvements such as ~30-40% latency reduction.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Implementation project manager with 10+ years delivering complex SaaS programs in healthcare and fintech, including payment integrations, onboarding, migrations, and operational/clinical workflow initiatives. Has owned 4-6 concurrent enterprise implementations at CVS and Fiserv, with strong executive reporting, governance, and cross-functional delivery through go-live.”