Vetted R Professionals

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Former Head of Growth & Strategy at a digital identity company (pre-MBA in Boston) who ran multiple cross-functional initiatives across sales, product, and finance. Delivered measurable outcomes including raising sales conversion from 70% to 83% in 30 days, achieving 17% profitability via a new pricing model, and launching a new onboarding platform GTM on time.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

“Customer-facing implementation/security-focused professional with experience at Freshworks and IMImobile (Cisco), advising enterprise customers on app security for CRM/ITSM and sensitive/PII workflows. Has led threat-model-driven remediation for API/RBAC/token risks and troubleshot complex cloud integration incidents (token expiry + load balancer misrouting) using logs/metrics/tracing, escalating with clear technical reports to accelerate engineering fixes.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

“Cofounded a travel app and built a production place recommendation + review system end-to-end using Next.js App Router and TypeScript, including Postgres-backed APIs and post-launch monitoring. Uses structured logging with Sentry and Vercel Analytics to diagnose issues and validate performance improvements, and has some exposure to Temporal-based workflow orchestration with retries/idempotency.”

Cristian Vega

Screened

Senior AI/ML Engineer specializing in Generative AI and RAG

California, null9y exp

“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”

Cary Burdick

Screened

Senior Data Scientist specializing in data engineering and analytics

Chicago, IL6y exp

“Data/NLP practitioner with experience in both financial services (Truist) and government (USDA), including an NLP-driven analysis of EU regulations to anticipate US regulatory focus and a major redesign/cleaning of complex pathogen lab-test public datasets. Built production data-quality pipelines with Dagster, Pandera, and Azure Synapse, and is comfortable validating hypotheses with historical backtesting and SME-driven quality controls.”

Hsi-Chun Wang

Screened

Mid-level Data Scientist specializing in LLM development and scalable ML pipelines

Remote4y exp

“Built and deployed production LLM pipelines for evidence-based scoring in two domains: biomedical literature mining (scoring ~2700 drug compounds vs gene targets/mechanisms) and long-horizon news analytics (35 years of Chinese articles). Emphasizes reliability at scale (retries/checkpointing/validation), rigorous empirical model benchmarking (GPT-4o/mini/5), and translating results into stakeholder-friendly visual narratives.”

Sundarajan Swaminathan

Screened

Senior Product Marketing Leader specializing in GTM strategy and RevOps analytics

Dallas, TX7y exp

“Growth creative/performance marketer from BYJU'S (India) who runs disciplined creative experimentation across Meta, TikTok, and YouTube. Notably shifted messaging from feature-led to outcome/testimonial-led creative, delivering CPA down 27%, ROAS up 35%, and +11% trial-to-paid conversion, and has experience leading a small creative pod (editors + writer) with a rapid-iteration production system.”

Fnu Pallavi Sharma

Screened

Intern Data Scientist specializing in ML, NLP, and MLOps for healthcare and enterprise AI

Madison, WI1y exp

“Built a production multi-cloud LLM-driven IT ticket automation system using LangGraph, Azure + Pinecone RAG, and an Ollama-hosted LLM on AWS, with Terraform-managed infra and PostgreSQL audit/state tracking for reliability. Also partnered with UW School of Medicine & Public Health students to deliver a glioma survival risk-ranking model, translating clinical feedback into practical pipeline improvements (imputation, site harmonization) and stakeholder-friendly visualizations.”

Ishwari Kulkarni

Screened

Intern IT & Data Analytics professional specializing in automation, cloud operations, and dashboards

Dallas, TX2y exp

“AppSec-focused engineer with experience spanning Accenture and a digital operations support internship, emphasizing secure SDLC and CI/CD security automation (SAST/DAST/SCA). Has hands-on troubleshooting experience using logs/metrics/APM traces (e.g., resolving DAST timeouts caused by rate limiting) and designs AWS/Kubernetes scanning integrations with least-privilege IAM, private networking, secrets management, and observability.”

shivapriya pillalamarri

Screened

Mid-level AI/ML Engineer specializing in financial analytics and production ML systems

Boston, MA4y exp

“Analytics candidate with experience in financial transaction and fraud detection projects, combining SQL data preparation, Python-based automation, and dashboarding. They have owned projects from stakeholder alignment and metric definition through rollout, with emphasis on reducing false positives, improving operational efficiency, and making analytics outputs easy for business teams to adopt.”

“Analytics professional at Santander Bank with hands-on experience building SQL and Python workflows for transaction reporting, reconciliation, and monitoring across messy multi-source financial data. They combine strong data validation and exception-handling practices with stakeholder-friendly dashboards, and also bring digital analytics experience from a Google Analytics UI optimization project focused on funnel drop-off and engagement.”

“Analytics professional with healthcare and operations experience who turns messy enterprise data from platforms like Teradata, GCP, SQL Server, and Snowflake into trusted reporting layers and reproducible analysis workflows. They combine SQL, Python, PySpark, Power BI, and Tableau to improve reporting accuracy and performance, including a 30% dashboard refresh improvement and 20-25% accuracy gains in healthcare reporting.”

“ML/AI engineer with production experience at S&P Global and Accenture, focused on regulated, enterprise-grade systems. Built end-to-end financial risk and credit default models with >90% precision and 12% fewer false positives, and is currently developing secure RAG pipelines on AWS SageMaker for enterprise insight extraction.”

“ML/AI engineer at PNC who has shipped enterprise-grade RAG and document intelligence systems for compliance and policy workflows. Stands out for combining LLM product thinking with production rigor—owning FastAPI/Kubernetes deployments, monitoring, evaluation, and human-feedback loops that drove measurable gains like 40% faster policy search and 30% faster compliance review.”

ASHISH DONDAPATI

Screened

Mid-level AI/ML Engineer specializing in Generative AI for Financial Services

San Francisco, USA6y exp

“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”