Vetted Snowflake Professionals

Vaishnavi M

Screened

Mid-level AI/ML Engineer specializing in MLOps and Generative AI

5y exp

“At Liberty Mutual, built a production underwriting decision assistant combining LLM reasoning with quantitative models and strong auditability. Implemented a claims-based response verification pipeline that cut hallucinations from 18% to 3% and materially improved user trust/validation scores. Experienced orchestrating ML/LLM workflows end-to-end with Airflow, Kubeflow Pipelines, and Jenkins, including SLA-focused pipeline hardening.”

Kunal Sanghvi

Screened

Mid-level Data Engineer specializing in AI, NLP, and LLM systems

USA3y exp

“Built and deployed a production AI customer support chatbot at Unique Design Inc. using FastAPI, AWS, Docker, and retrieval-based grounding on internal documents. Stands out for hands-on ownership across discovery, deployment, incident debugging, and post-launch iteration, with a strong focus on making LLM systems reliable and safe in real business workflows.”

Sowmya Mogireddy

Screened

Mid-level Data Analyst specializing in analytics, BI, and predictive modeling

CT, USA6y exp

“Analytics professional with cross-domain experience spanning healthcare claims, logistics optimization, and customer booking funnels. They combine strong SQL/Python execution with stakeholder alignment and operational adoption, and can point to measurable impact including 18% healthcare cost optimization and 24% logistics savings.”

“Python/backend engineer with a data-systems focus who actively integrates AI tools like ChatGPT, Copilot, and multiple coding assistants into development workflows. Stands out for using AI pragmatically across coding, debugging, SQL, testing, and documentation while maintaining hands-on ownership of validation, implementation, and code quality.”

“Paid media/SEM owner at Pulseway managing $180K–$230K monthly spend across Google Ads, Microsoft Ads, and Capterra, with a strong emphasis on MQL quality and MQL-to-opportunity conversion rather than lead volume. Runs disciplined, controlled testing across creative, keywords, audiences, and bidding (including Performance Max), and partners closely with sales feedback to reduce junk leads and improve pipeline contribution.”

“Backend/data engineer with hands-on production experience building a real-time notification API on Flask/Celery/Postgres and scaling it on AWS with Docker, Redis queuing, and SQLAlchemy query optimization. Also delivered AWS serverless deployments (Lambda) using Terraform + GitHub Actions and built AWS Glue ETL pipelines from S3 to Redshift with CloudWatch monitoring and DataBrew data quality checks.”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Startup-focused tech leader with 20+ years of experience who has led engineering teams across multiple startups and pitched inside VC firms. Currently building infrastructure for managing physical wealth—creating a canonical data layer that integrates with wealth management systems to account for precious metals and other tangible assets—and is prepared to raise capital to align with the right partners.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

“Data engineer who has owned production pipelines end-to-end, ingesting 50–100 GB/day from APIs/S3 and near-real-time Kafka into Snowflake with strong data quality gates (Great Expectations/dbt) and Airflow-based reliability (SLAs, alerting, dashboards). Also built a Snowflake-backed REST data API with caching/pagination and versioned endpoints, and designed a compliant, scalable web-scraping system with anti-bot handling and safe backfills.”

Anudeep Reddy Veerla

Screened

Mid-Level Software Engineer specializing in cloud-native microservices

Boston, MA3y exp

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

Hari Krishna Kona

Screened

Mid-level AI/ML Engineer specializing in Generative AI and LLM-powered NLP

Boston, MA3y exp

“LLM/AI engineer who built a production automated document-understanding pipeline on Azure using a grounded RAG layer, designed to reduce manual review time for unstructured financial documents. Demonstrates strong real-world scaling and reliability practices (Service Bus queueing, Kubernetes autoscaling, observability, retries/circuit breakers) plus rigorous evaluation (shadow testing, replaying traffic, multilingual edge-case suites) and stakeholder-friendly, evidence-based explainability.”

Srinivasan Gomadam Ramesh

Screened

Mid-level AI/Data Engineer specializing in agentic AI and data platforms

Redmond, WA7y exp

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

“Healthcare-focused ML/LLM engineer who built a production hybrid RAG workflow to automate prior authorization by retrieving from medical guidelines/historical cases (FAISS) and generating grounded rationales for clinicians. Strong in operationalizing ML with Airflow/Kubeflow/MLflow on SageMaker, optimizing latency (ONNX/quantization/async), and reducing hallucinations via evidence-only prompting; also partnered closely with clinical ops to deploy a readmission prediction tool used in daily rounds.”

“Data/ML engineer from HCLTech who modernized enterprise data by linking fragmented financial and supply-chain data across SAP/SQL Server/Snowflake using NLP entity linking and embeddings (FAISS). Delivered measurable impact including ~40% reduction in manual error-log triage and entity-linking accuracy improvements from ~86% to ~93%, with results surfaced in Power BI for real-time analytics.”

“Cloud-focused application security practitioner with hands-on AWS and Kubernetes experience, including securing a manufacturing monitoring platform (API auth, least-privilege IAM, CI/CD security checks) and troubleshooting a production data-ingestion outage caused by an overly restrictive IAM change. Experienced in implementing cloud-native security tooling (IAM Access Analyzer, Inspector, CloudWatch) and deploying monitoring/security agents via Kubernetes sidecars with Helm, Prometheus/Grafana, and Jenkins-driven CI/CD.”

“Data engineer who has owned production pipelines end-to-end—from Kafka/Airflow ingestion through SQL/Python validation and dbt transformations into Redshift/BI. Also built and operated a large-scale distributed web scraping platform (50–100 sites daily, ~5–10M records/day) with Kubernetes, Kafka queues, robust retries/DLQ, anti-bot measures, and backfill-safe raw HTML storage.”

Arjun Shrestha

Screened

Intern AI/GenAI Engineer specializing in NLP, RAG, and Snowflake Cortex

Columbus, Ohio1y exp

“Built and deployed a production AI invention/patent review platform that compares invention submissions against patent rules to provide instant feedback, reportedly cutting legal team review time by ~80%. Learned Snowflake Cortex LLMs and production deployment (Docker + AWS) on the job, and validated system quality through human-in-the-loop testing with experienced legal stakeholders.”

Shiva Dasi

Screened

Mid-level Software Engineer specializing in LLM agents and cloud-native systems

Boston, MA4y exp

“Built and shipped production LLM agents in compliance-sensitive environments (FERPA), emphasizing reliability via structured outputs, state-graph orchestration (LangGraph), and CI-driven eval/regression testing. Also has experience hardening messy ERP ingestion pipelines at scale (50K monthly orders) with normalization, idempotency/deduplication, and robust failure handling using AWS (SQS/CloudWatch) and PostgreSQL.”