Vetted R Professionals

“Data engineer/backend engineer who owns large-scale, real-time event pipelines on AWS end-to-end, including a petabyte-scale CDC ingestion flow from multiple Postgres DBs into Redshift. Re-architected a legacy DynamoDB+S3 approach into a Delta Lake + DuckDB/PyArrow-compatible design, improving performance dramatically (e.g., ~600s to ~10s for 1k records) and increasing reliability at high file volumes.”

Jincheng Pang

Screened

Principal Data Scientist specializing in healthcare analytics and medical imaging AI

Sudbury, MA11y exp

“Developed an LLM-driven recommendation agent in Azure Databricks to triage oncology patients and trigger second-opinion case creation using medical claims and EHR data. Uses ICD-10/CPT/J-code features in prompts, embeddings + vector DB similarity, and a backtesting framework emphasizing recall to avoid missing clinically relevant cases while supporting business revenue.”

Benjamin Hudnut

Screened

Junior Live Events & Music Operations professional specializing in booking, touring, and community events

Los Angeles, CA4y exp

“Founder/director of the music and event production company Tuned In, personally driving outbound BD to book underground LA talent and produce 25 events, including a major Lunar New Year collaboration with Alice Longyu Gao. Also ran multi-channel voter/community outreach for the Frankie Carrillo campaign (door knocking, 1,000+ SMS) and built automation tools (Apple widget; AI image-to-CSV workflow at Live Nation) to improve operational efficiency.”

Jahnavi Bellapukonda

Screened

Mid-Level Software Engineer specializing in full-stack web and cloud systems

Remote, USA3y exp

“Full-stack engineer with strong data engineering and privacy-domain experience, having owned an automated Data Subject Rights (DSR) processing pipeline end-to-end across Azure SQL and GCP (GCS/BigQuery). Emphasizes production reliability via idempotency, validation checkpoints, structured logging/monitoring, and safe CI/CD-driven deployments, and has also built React+TypeScript + Node/Postgres web apps with scalable, maintainable architecture.”

Niteesh Ganipisetty

Screened

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and Computer Vision

Grand Rapids, MI4y exp

“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”

Mounika Gunturu

Screened

Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms

New York, NY9y exp

“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”

“Founder/CTO-type startup leader who has built cloud-native data and AI platforms from scratch while owning both technical vision and product direction. Brings rare end-to-end startup experience spanning zero-to-one building, growth-stage execution, and fundraising from early stage through exit, with a strong ability to translate technical complexity into clear investor narratives.”

“Analytics-focused candidate with hands-on experience building SQL data pipelines and Python-based forecasting workflows for inventory and planning use cases. They emphasize data quality, stakeholder trust, and operational adoption, citing a 19% forecast accuracy improvement and strong experience translating analytics into dashboard-ready business metrics.”

Bhaskar Reddy

Screened

Junior Business & Data Analyst specializing in analytics and BI

Fairfield, CT2y exp

“Analytics-focused candidate with hands-on experience building SQL and Python workflows that turn messy multi-source data into reporting assets and dashboards. They show strong practical judgment around data quality, table grain, validation, and performance tuning, and they described an education-focused engagement project that reportedly improved course completion by 15% through targeted interventions and metric-driven stakeholder alignment.”

“Candidate has hands-on experience delivering production data and AI systems, including an AWS-based real-time data platform for a financial client at Deloitte and a production RAG workflow that cut manual search time by 40%. They stand out for combining strong data engineering depth with practical LLM governance, incident debugging, and stakeholder management across business and risk/compliance teams.”

“Analytics professional with Deloitte experience building SQL and Python workflows for revenue, pipeline, and opportunity analytics at scale. They combine strong data engineering and modeling skills with business-facing delivery, citing impacts including 8-10% conversion improvement, ~$700K revenue protected, 12% YoY project acquisition growth, and 15% retention improvement in financial services.”

“Senior engineering executive who has spent the last decade leading large-scale platform modernization while staying deeply hands-on in architecture, debugging, and code reviews. He has led 120+ person organizations, built cloud-native SaaS and identity platforms across regulated and enterprise domains, and ties engineering decisions directly to revenue outcomes, including contributing to Wind River's $1B+ milestone.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”

“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”

“ServiceNow engineer who built and launched a production LLM-powered ticket resolution/knowledge assistant using RAG (LangChain + Hugging Face embeddings + vector search) integrated into internal support dashboards via REST APIs. Optimized the system from ~6–8s to ~2–3s latency while improving usability with concise, cited answers and guardrails (grounding + similarity thresholds), delivering ~30–35% reduction in manual ticket investigation effort.”

Pooja Dokuri

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps

Remote, USA4y exp

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

Divyam Agrawal

Screened

Mid-level Machine Learning Engineer specializing in LLMs and NLP classification systems

Seattle, WA4y exp

“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”

Thomas To

Screened

Mid-level Full-Stack Engineer specializing in AI/ML data platforms for biotech and FinTech

Emeryville, CA6y exp

“AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.”