Vetted BigQuery Professionals

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“AI-native software developer who has built a highly structured workflow around Claude, Cursor, design agents, and SpecKit to plan, design, implement, and test features end to end. They also use multi-agent setups with sub-agents and git worktrees, and have experience acting as a tech lead for AI agents by assigning roles, guiding execution, and reviewing outputs.”

“Frontend/platform-leaning engineer who owned a shared React component library (design-system style) adopted across multiple SPAs, with strong discipline around SemVer, deprecation strategy, and developer documentation. Also has backend/distributed-systems experience diagnosing and fixing a Kafka microservice race condition and has led prioritization/migration planning in an unstructured monolith-to-microservices environment (Centene).”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Infrastructure engineer with hands-on ownership of a scaled IBM Power/AIX estate (AIX 7.x, VIOS, HMC; 2 frames/20+ LPARs) supporting critical middleware and database workloads, including live DLPAR changes and VIOS/SAN outage recovery. Also brings modern DevOps/IaC experience building GitHub Actions pipelines for Docker/Kubernetes deployments and provisioning AWS environments with Terraform (EKS/RDS/VPC/IAM) using modular, review-driven workflows.”

“Research Assistant at the University of Houston who built and live-deployed a production RAG system for 1000+ research documents, using hybrid retrieval (dense+BM25+RRF) with cross-encoder reranking and RAGAS-based evaluation; reported 66% MRR, 0.85+ faithfulness, and 68% lower LLM inference costs. Also built a deployed LangGraph multi-agent research system (Researcher/Critic/Writer) with tool integrations (Tavily, arXiv) and dual memory (ChromaDB + Neo4j), plus freelance automation work delivering a WhatsApp chatbot and n8n workflows for a wholesale clothing business.”

“Data engineer focused on sales/marketing analytics pipelines, owning ingestion from CRMs/ad platforms through warehouse serving and dashboards at ~hundreds of thousands of records/day. Built reliability-focused systems including dbt/SQL/Python data quality gates with alerting, a resilient web-scraping pipeline (retries/backoff, anti-bot tactics, schema-change detection, backfills), and a versioned internal REST API with caching and strong developer usability.”

“Data engineer with Wells Fargo experience owning an end-to-end lakehouse ETL pipeline on Databricks/Azure Data Factory, processing ~480GB daily and implementing robust data quality/reconciliation across 40+ tables to reach ~99.3% reliability. Strong in performance optimization (cut runtime 5.5h→3.8h), CI/CD and monitoring, and resilient external/API ingestion with retries, schema validation, and backfills.”

Bhakti Patel

Screened

Senior Full-Stack Software Engineer specializing in .NET, Python, and cloud-native systems

Worcester, MA11y exp

“Full-stack engineer who owned an end-to-end production feature for a Piraeus Bank stock exchange module, spanning React/TypeScript, backend services, and cloud operations with Docker + CI/CD, delivering reported 90% faster API responses and improved uptime. Also built a Smartwound research MVP on AWS, creating a Python image-processing/scoring pipeline to ship despite unclear image-analysis specs.”

Aishwarya Thorat

Screened

Intern Data Scientist specializing in ML engineering and LLM agentic workflows

San Francisco, CA6y exp

“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”

“Healthcare analytics professional with TCS/Humana experience turning messy claims and eligibility data into reliable reporting assets using SQL and Python. They combine strong data engineering and analytics execution with stakeholder management, including automating monthly claims reporting from half a day to under 5 minutes and driving a provider outreach effort that reduced claim rejection rates by about 20%.”

“ML/AI engineer with hands-on ownership of production recommendation and RAG systems at Northern Trust. They combine transformer modeling, latency optimization, cloud deployment, and monitoring with measurable business impact, including 14% accuracy gains, 12% engagement improvement, and 19% better query relevance.”

“Built end-to-end ML and GenAI systems at Northern Trust, including a production RAG-based document intelligence platform for financial reports and contracts. Stands out for combining strong MLOps execution with practical product judgment—improving forecast accuracy by 22%, document review accuracy by 38%, and cutting deployment time by 45% while keeping latency and reliability production-ready.”

“Backend/data engineer focused on production-grade Python microservices and AWS platforms, including a hybrid Lambda + ECS Fargate architecture managed with Terraform and CI/CD. Has hands-on reliability experience (JWT/OAuth, timeouts, retries, centralized error classification) and built AWS Glue/PySpark ETL pipelines consolidating PostgreSQL/RDS, MongoDB, and S3 sources into curated partitioned Parquet datasets. Demonstrated measurable SQL tuning impact (8 minutes to 25 seconds) and disciplined legacy-to-modern migrations with parity validation and UAT sign-off.”

“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”

“LLM/agentic systems engineer who built a production "Agentic AI Diagnostic Assistant" for network engineers, using a multi-agent Llama 2 + LangChain architecture with RAG over telemetry/incident data in DynamoDB and confidence-based deferrals to reduce hallucinations. Also has strong MLOps/orchestration experience (Airflow, EventBridge, Spark, Docker, SageMaker/ECS) at multi-terabyte/day scale and delivered multilingual NLP analytics (fine-tuned BERT/spaCy) for support operations through hands-on stakeholder workshops.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“ML/NLP practitioner with banking experience (M&T Bank) who has built a GPT-4 RAG system using LangChain and Pinecone to connect unstructured customer data with internal knowledge bases, improving accuracy and reducing manual lookup time by 50%+. Strong in entity resolution and productionizing scalable Python data workflows, including major performance wins by migrating bottleneck joins from Pandas to Dask.”

“Full-stack engineer with healthcare and finance experience who has owned end-to-end production systems across Azure and AWS. Built a real-time clinical dashboard at Centene (React + FastAPI + Azure Event Hubs) that cut data latency from ~12 minutes to under 1 minute and was associated with a 30% reduction in intervention delays. Also delivered MVPs in high-ambiguity environments at Accenture during monolith-to-microservices migration, improving uptime and maintainability with measurable results.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

Manikanta Kopparapu

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native web applications

TX, USA4y exp

“Full-stack engineer who has owned customer-facing and internal web portals end-to-end (API, database, React UI, and deployment). Experienced designing multi-service architectures with Node/Express and Java/Spring Boot plus RabbitMQ/Kafka messaging, emphasizing contract/versioning discipline, observability, and operational tooling that measurably reduces support load and manual work.”