Vetted Snowflake Professionals

“At CVS Health, the candidate productionized a RAG-based LLM solution in a regulated healthcare setting, emphasizing reliable data pipelines, LoRA fine-tuning, monitoring, safety guardrails, and A/B testing. They have hands-on experience troubleshooting real-time RAG failures (e.g., chunking/embedding issues) and regularly lead developer-focused demos/workshops while translating technical architecture into business value for stakeholders.”

“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.”

sarah robert

Screened

Staff RPA & Automation Engineer specializing in Financial Services

Baton Rouge, LA11y exp

“Blue Prism RPA developer in a small FinTech-aligned team who owned ~20 production bots and drove both delivery and reliability. Built a shared VDI/locking design that cut infrastructure cost ~20–30% and routinely handled ServiceNow-driven production incidents end-to-end, including hotfixes and longer-term SDLC fixes. Also acted as a player-coach, training junior hires and maintaining high bot success rates (up to 99% within SLA).”

“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.”

Sree Damineni

Screened

Mid AI/ML Engineer specializing in financial and insurance analytics

USA3y exp

“Senior AI/ML engineer focused on production ML, LLMs, and MLOps, with concrete experience shipping fraud detection and enterprise RAG systems. They combine strong deployment and monitoring discipline with measurable business impact, including 31% precision improvement in fraud detection and 37% better answer relevance in a financial-document QA system.”

“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.”

“Product leader with experience at Quest Diagnostics and Citeline, spanning healthcare, regulatory, and AI-enabled products. Particularly strong in rebuilding complex platforms, designing human-in-the-loop AI workflows, and making pragmatic product decisions in high-trust, real-world environments such as offline clinical mobile apps and regulated document-import systems.”

“Full-stack and AI product engineer with strong AWS/Snowflake experience who built an internal feature flag platform and helped migrate a cybersecurity insights product into a multi-agent AI chat interface. They report production scale of 1M+ embeddings and 50k+ monthly queries, with outcomes including an 80% reduction in analyst work and dashboard generation in 7 minutes; the work was also featured by Claude and AWS.”

Bibek Poudel

Screened

Mid-level Full-Stack Developer specializing in FinTech and E-commerce

Artesia, CA4y exp

“Full-stack engineer with experience spanning enterprise AI and e-commerce, including building an agentic orchestration platform and owning a RAG system pipeline at JP Morgan Chase. Stands out for combining React/Node backend delivery with LLM security work, RBAC-heavy system design, and practical SQL/query optimization in production.”

“Backend engineer from Optum who built and optimized a real-time, Kafka-driven healthcare claims processing platform handling 1M+ claims/month. Strong in reliability, state management, and observability for distributed systems, plus production deployment automation with Docker/Kubernetes and CI/CD; no direct ROS/robotics simulator experience yet but frames work in robotics-adjacent real-time principles.”

“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.”

“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.”

“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.”

“Full-stack engineer with financial services experience (Lincoln Financial) who owned a customer-facing financial portal end-to-end using TypeScript/React and Node/Express. Has hands-on microservices and RabbitMQ event-driven workflows, addressing scale issues like retries/duplicates with idempotency and traceable logging, and built an internal real-time ops/support dashboard to improve monitoring and incident response.”

“Built production LLM/RAG platforms for financial services to enable natural-language Q&A over large policy/compliance document sets stored in Snowflake and SharePoint. Strong in MLOps and orchestration (Airflow, ADF, Step Functions, MLflow) and in solving real production issues like stale embeddings and model performance, including an incremental Snowflake Streams sync that cut processing time from hours to minutes.”

“Built a production internal LLM/RAG assistant at CVS Health to cut time spent searching long policy and clinical guideline PDFs, combining fine-tuned BERT/GPT models with FAISS retrieval and a FastAPI service on AWS. Demonstrates strong real-world reliability work (document cleanup, hallucination controls, monitoring/drift tracking with MLflow) and close collaboration with non-technical clinical operations teams via demos and feedback-driven iteration.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“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.”

“Infrastructure/operations engineer with hands-on production IBM Power/AIX (AIX 7.x, VIOS, HMC) and PowerHA/HACMP clustering experience, including DLPAR changes, failover testing, and incident recovery. Also delivers modern cloud DevOps work—GitHub Actions CI/CD for Docker-to-Kubernetes on AWS and Terraform-based provisioning of core AWS infrastructure (VPC/EKS/RDS/IAM) with controlled rollouts and drift checks.”