Vetted Tableau Professionals

Bhoomi Parikh

Screened

Junior Product Manager specializing in AI-enabled analytics products

Mountain View, CA2y exp

“Product/full-stack engineer with analytics-dashboard experience at Kantar, owning features end-to-end from React/Next.js UI through Postgres data modeling and query optimization. Built a multidimensional filters/tags module that cut analyst discovery time by ~60% and also implemented durable backend workflows for bulk report generation with retries and idempotency, validated via EXPLAIN ANALYZE and production monitoring.”

Shabari Vignesh

Screened

Mid-level Data Engineer specializing in cloud data platforms and AI agents

Santa Clara, CA6y exp

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

Farida Poor

Screened

Junior Machine Learning Engineer specializing in NLP and multimodal transformers

Bay Area, CA3y exp

“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”

Snehitha Penumaka

Screened

Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines

Dallas, TX3y exp

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”

Pravalika Kuppireddy

Screened

Mid-level AI/ML Engineer specializing in Generative AI and intelligent automation

4y exp

“LLM engineer who built and productionized a system to classify GitHub commits (performance vs non-performance) using zero-/few-shot approaches over commit messages and diffs, working at ~5M-record scale on multi-node NVIDIA GPUs. Experienced orchestrating end-to-end LLM pipelines with Airflow and GitHub Actions, and emphasizes reliability via testing, guardrails, and observability while collaborating closely with non-technical product stakeholders.”

Rich Brebes

Screened

Senior Lifecycle Marketing Manager specializing in retention and CRM automation

Los Angeles, CA10y exp

“Lifecycle/CRM marketer with DTC jewelry experience who built a segmented post-purchase workflow targeting engagement-ring buyers and drove repeat purchases from ~2% to ~30% within a 90-day window using personalized recommendations, educational/social-proof content, and incentive testing. Emphasizes rigorous segmentation, ongoing data audits, and continuous A/B testing to improve retention and revenue.”

“Built and shipped a widely adopted, production-grade RAG internal search assistant that unified scattered engineering knowledge, deployed as a FastAPI service on Kubernetes with FAISS + LangChain. Demonstrates deep practical expertise in retrieval tuning (chunking, hybrid search, re-ranking) and in making LLM workflows reliable in production via guardrails, monitoring, and evaluation, plus strong cross-functional delivery with non-technical operations teams.”

“Data analyst with cross-industry experience spanning insurance analytics at L&T Infotech and experimental imaging analytics at Mylyser. Stands out for building scalable SQL/PySpark data pipelines, standardizing business-critical metrics like claims lifecycle and policy retention, and delivering measurable impact such as 50%+ faster query performance and a 15% reduction in claims settlement time.”

“Former Database Marketing Analyst with hands-on SQL experience supporting targeted casino marketing campaigns and a strong emphasis on validation accuracy. Also describes owning analytics work that standardized engagement metrics across teams, automated cohort/segmentation reporting, and helped identify onboarding friction that improved user engagement.”

David Alvarado

Screened

Junior Business Analyst specializing in data analytics and BI

Orlando, FL3y exp

“Analytics candidate with insurance domain experience at Chubb, combining strong SQL/Python data engineering for claims reporting with business-facing metric design in Power BI. Also built an MLB game outcome predictor that beat Vegas implied probabilities using public data, showing strong product thinking and applied modeling ability beyond standard BI work.”

“Analytics professional with experience spanning financial services and e-commerce, including NAV reconciliation/reporting at State Street and revenue optimization work on checkout and retention. Stands out for combining strong SQL/Python data engineering with business-facing analytics, delivering measurable gains like 3-5% conversion improvement and 8-10% payment success uplift.”

“Built OpenGPU features spanning React/TypeScript, Go orchestration, PostgreSQL, Redis, and Stripe, with a strong focus on reliability, transaction integrity, and low-latency distributed systems. Also shipped LLM product infrastructure, including persona-conditioned frameworks and reusable prompt/model abstractions, showing a blend of systems engineering and fast product iteration.”

“Built end-to-end AI products across fintech and healthcare, including a real-time loan risk prediction system and a patient feedback insights platform. Stands out for combining full-stack delivery, production ML/MLOps on AWS, and pragmatic human-in-the-loop safeguards; reported a 22% improvement in prediction accuracy.”

“Chief of Staff/operator who joined venture-backed startup Blockless at the pre-seed stage and built the operating system from scratch while acting as the founder’s right hand. Combines startup execution, fundraising support, and deep Web3/DeFi domain fluency, including helping close a $3.5M seed round ahead of schedule with clean diligence materials.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“ML/MLOps practitioner from Discover Financial who built and deployed a real-time AI fraud detection platform (LSTM + VAE) on AWS SageMaker with Docker/FastAPI and Jenkins-driven CI/CD. Demonstrated measurable impact (30% accuracy lift, 25% fewer false alerts) and deep expertise in class-imbalance mitigation, drift monitoring, and orchestration (Airflow/Kubeflow), plus strong stakeholder adoption via Power BI dashboards for fraud/compliance teams.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Worked on large financial analytics datasets combining complaint text, transaction logs, and demographics; built end-to-end NLP/ML pipelines (TF-IDF + Random Forest) and data integration in BigQuery with Tableau reporting, citing ~95–98% accuracy. Also implemented entity resolution with fuzzy matching and semantic linking using BERT sentence-transformer embeddings stored in FAISS, including fine-tuning on labeled pairs to improve search/linking relevance.”

“Built an Arduino-based obstacle-avoiding robot using sonar/laser sensors and improved performance from 0.60 to 0.87 accuracy through sensor-fusion thresholding and iterative tuning. In an internship, optimized a legal-document NLP pipeline by switching to a distilled/quantized transformer and offloading inference to a GPU-backed Flask service, cutting inference time by 40%+ without added infrastructure spend.”

“Backend/platform engineer with State Farm experience modernizing and scaling an enterprise consolidated payment data platform and event-driven pipelines. Built cloud-native payment architecture (ECS->EKS) handling millions of financial transactions/day and high-volume telemetry (~100M events/day), with strong schema governance (Avro + schema registry) and production operations/incident mitigation driven by observability.”