Vetted Snowflake Professionals

“Player-coach engineering leader currently running a Solution Architecture/FDE team responsible for both presales and postsales delivery. Stands out for combining enterprise systems thinking with hands-on AI product work: they built configurable tooling that sped delivery by ~30%, drove a Kafka-to-Pulsar architecture shift for scale, and spent the last two years building LLM-based document extraction and RAG inference pipelines shaped directly by user feedback.”

“AI/ML engineer in financial services who has built both LLM-powered compliance tools and production fraud/credit risk systems at Ally Financial. Particularly strong in regulated, high-stakes environments: combines RAG/LLM architecture, rigorous evaluation, and human-in-the-loop governance, and also helped stand up a unified ML platform from scratch.”

“Data/automation-focused engineer with hands-on experience building production workflows across marketing, sales, and RevOps at ZoomInfo. They’ve owned end-to-end automations spanning Snowflake/Databricks pipelines, ad platform API integrations, LLM-powered sales prep and deal summarization, and ML-based account prioritization.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“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.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“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.”

“Built and productionized an autonomous research agent (AutoGPT) in a Docker/Kubernetes environment with Pinecone-based long-term memory and custom Python tools for analysis, visualization, and report drafting. Implemented layered guardrails (prompt templates, automated validation, self-critique loops, and monitoring) and achieved ~25% reduction in manual report generation time while scaling the workflow to support multiple concurrent users.”

“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.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“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.”

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Allan Farinas

Screened

Senior Full-Stack Software Engineer specializing in Python and AWS

West Covina, CA11y exp

“Backend/data engineer who has built production Python microservices (FastAPI) and AWS-native platforms for event ingestion and analytics, combining ECS/Fargate + Lambda with CloudFormation-driven environments and strong secrets/IAM practices. Experienced modernizing legacy logic with parallel-run parity validation and safe phased cutovers, and has demonstrated measurable SQL tuning wins (20–30s down to 1–2s) plus incident ownership in Glue/Step Functions ETL pipelines.”

Ethan Lam

Screened

Junior Software Engineer specializing in data platforms and full-stack development

Toronto, Ontario3y exp

“Software engineer with Warner Music Group experience owning and shipping analyst-facing data products (marketing/streaming data dashboards) end-to-end with high adoption through continuous stakeholder feedback. Also builds side projects with TypeScript/React and domain-driven API design, emphasizing flexibility (including swapping databases mid-development) and pragmatic microservices reliability patterns (logging, timeouts, retry backoff).”

Vineeth Reddy Vallapureddy

Screened

Mid-level Full-Stack Software Engineer specializing in backend microservices and enterprise AI tools

Redwood City, California5y exp

“Backend/platform engineer with experience across C3.ai (supply chain demand planning) and Amdocs (telecom), working on large-scale data systems and microservices. Has driven first-time adoption experiments of Snowflake + Spark to handle billion-record workloads, built Jenkins-to-Kubernetes delivery pipelines with Nexus artifact management, and implemented Kafka streaming between microservices with HA and retry/error-handling patterns.”

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.”

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.”

SaiTeasmitha Kaja

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring Boot and cloud microservices

Houston, TX4y exp

“Backend-focused Python/Flask engineer who has built authentication/profile services with clean modular architecture (blueprints + service layer) and tuned SQLAlchemy/Postgres for scale using indexing, query rewrites, and pagination. Has production-style integration experience for AI/ML via TensorFlow Serving and OpenAI APIs (batching, rate limiting, caching), plus multi-tenant data isolation and high-throughput background processing with Celery/Redis and idempotent jobs.”

John Hoffman

Screened

Senior Data Engineer specializing in Databricks, Spark, and AWS for government healthcare data systems

Windsor Mill, MD12y exp

“Python/AWS engineer focused on batch-processing and data workflows, including building reusable S3/boto3 utilities with reliability features and IAM-based auth. Has led low-risk legacy modernizations using parity testing plus a month of parallel production runs, and has owned production issues end-to-end (including fixing a client-side Excel macro) while contributing to significant AWS cost reductions (~$10k/month).”

Prasanna Chelliboyina

Screened

Mid-level Machine Learning Engineer specializing in forecasting, NLP, and GenAI

United States6y exp

“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”

Sai Charan Kolla

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps on AWS

TX, USA5y exp

“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”

Samatha Amsala

Screened

Mid-level Data Engineer specializing in cloud data warehousing and analytics

Omaha, NE6y exp

“Data engineer at American Express who owned end-to-end pipelines for transaction and customer data used in finance reporting and risk analytics, processing ~5–8M records/day. Built Airflow-orchestrated ingestion (including external APIs/web sources) with strong data quality controls, monitoring/alerts, and resilient backfill/retry patterns, and also shipped a versioned REST API serving aggregated metrics to analytics teams.”