Vetted Data Cleaning Professionals

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”

“ML practitioner with hands-on experience in healthcare time-series modeling (CGM-based blood glucose prediction) including a novel ICA-based blind source separation approach and robust data-cleaning for noisy, missing sensor data. Also built an embeddings + LLM-powered podcast recommendation workflow using YouTube transcript scraping and Vellum AI document indexing, with a strong emphasis on production-grade engineering practices (TDD, monitoring) and realistic rolling validation for forecasting.”

“ML/NLP practitioner who built customer-feedback topic modeling (NMF + TF-IDF) to diagnose chatbot-to-agent handovers and drove product/ops changes that reduced operational costs by 20%. Also developed LSTM-based intent recognition using Word2Vec/GloVe embeddings for semantic linking, and deployed an LSTM autoencoder for fraud anomaly detection that cut false positives by 25% while capturing 15% more fraud in A/B testing.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

SUMIT MAMTANI

Screened

Mid-level Data Scientist specializing in ML, MLOps, and customer analytics

Tempe, AZ4y exp

“ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.”

Saniya Shinde

Screened

Mid-level Data Scientist specializing in NLP, LLMs, and RAG systems

Washington, DC4y exp

“Built and deployed a production-style vision-language pipeline that generates structured medical reports from chest X-rays using BioViLT embeddings, an image-text alignment module, and BiGPT fine-tuned with LoRA, delivered via Streamlit and hosted on AWS EC2. Also collaborating experience presenting EDA findings, feature importance, and model performance to Ford managers while working with vehicle parts data at Bimcon.”

Akhilesh Babu Tumati

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-integrated systems

3y exp

“Built and deployed a Virginia Tech CS department blog/archive application using a MERN/Next.js stack and a fully serverless AWS architecture (Lambda, API Gateway, S3, CloudFront, Route 53), including CI/CD via the Serverless Framework. Implemented RBAC for student/faculty/admin users and added an article export feature backed by MongoDB.”

“Founder building an AI-driven predictive analytics and programmatic advertising platform focused on identifying and marketing "hidden gem" songs and books. Has demonstrated early traction with 1M+ streams, a growing revenue base, acquired catalogs, and an artist roster, while developing proprietary AI and a supporting data ecosystem.”

“Former founding engineer and eventual CTO at 2Plus2 Partners (25 years ago) with additional experience in two private-equity-backed companies (apex analytix and HICX). Interested in helping build another company before retirement; comfortable with entrepreneurial risk but cannot self-fund significant capital.”

“Analytics professional with Bank of America experience analyzing the digital card replacement journey at scale, transforming 20M event records across 900K clients into actionable reporting and recommendations. Stands out for combining SQL/Python data engineering, customer journey analytics, and executive-facing storytelling strong enough to get senior leadership to adopt most of their proposed app improvements.”

Christopher Schmitt

Screened

Executive Technology Leader specializing in FinTech & SaaS platforms

Atlanta, GA29y exp

“Startup-oriented technology leader with 13 years in early-stage environments, including serving as CTO through Series A and Series B fundraising. Has direct experience presenting to investors and a board of advisors, and is especially motivated by taking products from idea to first sale and scaling them into real businesses.”

“Healthcare-focused data analyst with hands-on experience at Molina Healthcare building SQL and Python workflows for retention and churn analytics. They combined enrollment, CRM, and claims data into Power BI reporting, automated predictive churn analysis, and tied their work to measurable outcomes including faster processing, better reporting accuracy, and reduced churn.”

Anthony Thanpoovong

Screened

Entry-level Software Engineer specializing in cloud, AI, and full-stack development

Toronto, Canada1y exp

“Backend/AI engineer with hands-on experience building LLM-powered data products and AI platform workflows, including a project that turns tabular datasets into graphs, summaries, and chat-based insights with 1-2 second latency. Also contributed at TELUS to a Sovereign AI Factory self-serve onboarding platform tied to 100+ NVIDIA H200 GPUs, giving them an interesting mix of applied LLM, platform, and infrastructure exposure.”

Rami Jaloudi

Screened

Senior Applications Engineer specializing in legal technology and eDiscovery

New York, NY16y exp

“Early-stage founder candidate exploring an AI-enabled legal tech startup focused on document intelligence, secure workflows, and enterprise automation. Brings a rare blend of technical architecture fluency and product/business thinking, with clear firsthand insight into legal and document-heavy operational pain points.”

Sriram Degala

Screened

Senior AI/ML Engineer specializing in healthcare and finance AI

Dallas, TX4y exp

“Built production-grade medical AI systems at MD Anderson, including an end-to-end RAG chatbot used by clinical researchers for real-time drug interaction and trial literature queries. Stands out for combining healthcare domain knowledge with strong MLOps, evaluation, and safety practices, and for delivering measurable gains in latency, retrieval precision, and team adoption.”

“Healthcare analytics candidate with hands-on experience turning messy claims and clinical data into validated SQL/Python pipelines and Power BI dashboards. They have delivered measurable impact in revenue cycle operations, including 15-18% improvement in reimbursement accuracy and 40-45% reduction in manual reporting effort.”

Chuangyu Xu

Screened

Entry-Level Software Engineer specializing in Java backend and distributed systems

New York, NY

“Built and shipped a production AI mock-interview platform where an LLM “interviewer” generates role-specific questions, runs multi-turn follow-ups, and outputs structured scoring/feedback using RAG. Demonstrates strong production-readiness practices (Prometheus/Grafana monitoring, retries/timeouts, fallback models/templates, schema validation, replay-based debugging) and achieved 99%+ availability with ~40% higher session completion. Also has experience integrating agents with messy SAP/ERP-style data sources using a data middleware layer, cleaning/validation, and idempotent write safeguards.”

“Built and owned an end-to-end RAG-based AI support platform at Mechanize (FastAPI/LangChain/Pinecone/React) with rigorous evals and guardrails, driving 45% fewer support tickets and ~$280K annual savings. Also led a high-risk legacy modernization at Argo AI, incrementally extracting a monolithic Django backend using Strangler Fig + feature flags while supporting 10K+ concurrent users.”

“Built an end-to-end GenAI underwriting copilot at TD Bank for complex financial documents, combining RoBERTa-based risk classification with Azure OpenAI RAG to deliver grounded, citation-based insights. Drove a 40-50% reduction in manual underwriting review time and created reusable FastAPI ML services that cut integration effort for other teams by 30-40%.”