Vetted Snowflake Professionals

Sriram Krishna

Screened

Mid-Level Software Engineer specializing in AI/ML and cloud-native platforms

Redmond, WA5y exp

“Backend/AI engineer who has built production LLM orchestration and agentic workflow systems in Python/FastAPI on Kubernetes across AWS/Azure. Demonstrated strong reliability engineering by debugging a real-world memory retention issue that caused latency spikes/timeouts, and strong data/performance chops with a PostgreSQL optimization that cut query latency from ~1.2s to ~15ms. Targets roles building scalable, guardrailed AI-driven workflow automation with robust observability and human-in-the-loop controls.”

“Data analytics and BI professional with experience turning messy institutional and customer data into decision-ready reporting and predictive systems. They combine strong SQL/Python execution with end-to-end ownership of churn analytics, stakeholder alignment, and operational rollout into dashboards and CRM workflows.”

“Built and owned a production-grade RAG/LLM support automation system on AWS using GPT-4, Pinecone, FastAPI, and Redis, taking it from initial experimentation through deployment, monitoring, and iterative improvement. Their work reduced support workload and ticket volume by about 40%, improved CSAT and self-service resolution, and they also created shared Python/LLM infrastructure that accelerated other teams' delivery from weeks to days.”

“Software engineer with a pragmatic, production-oriented approach to AI-driven development, using AI to accelerate coding while keeping human oversight on correctness, architecture, and final decisions. Has hands-on experience with agent-style AI workflows and has led the design and coordination of AI-agent systems with a strong emphasis on reliability, performance, and end-to-end execution.”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Data engineer with experience at Morgan Stanley and Star Health owning production-grade lakehouse pipelines for credit risk and healthcare datasets. Built Azure/Databricks/Delta/Snowflake-based platforms processing millions of records per day with strong data quality, observability (Monte Carlo/Azure Monitor), and reliability practices, plus experience delivering curated data services with performance tuning and backward-compatible versioning.”

Gomathy Selvamuthiah

Screened

Junior Data/AI Engineer specializing in MLOps, real-time pipelines, and LLM applications

Portland, US2y exp

“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”

Mayank VYAS

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG retrieval, and IoT ML systems

Tempe, AZ4y exp

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

“Product-minded full-stack engineer with experience in fintech identity verification and industrial analytics, focused on turning repeated operational pain points into reusable platforms. Built real-time KYC/KYB dashboards, secure cross-platform web components, and a multi-tenant workflow engine that cut onboarding from 2 weeks to 1 day while materially improving conversion, reliability, and developer speed.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

Tanvi Kulkarni

Screened

Intern Full-Stack Engineer specializing in Java, React, and cloud-native backend systems

Santa Clara, CA1y exp

“Frontend-focused engineer with startup experience (SmartPath, OPC AI) who owned and evolved an internal React/TypeScript component library treated like OSS—refactoring core form and API wrapper modules for stability, type safety, and smaller bundles. Comfortable diagnosing production issues via logs/API traces and shipping end-to-end fixes with tests and documentation, including internal workshops to drive adoption.”

“Marketing and product-focused analytics candidate with hands-on experience turning messy large-scale data from Hadoop/HDFS, Azure Data Lake, and transaction systems into validated reporting tables. They combine SQL and Python automation with strong metric design, cohort/retention analysis, and stakeholder-friendly dashboards, including a reported 30% query performance improvement and weekly reporting automation.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

“Backend engineer who has led redesigns and migrations for a real-time logistics platform, improving scalability and resilience while managing eventual consistency tradeoffs. Demonstrates strong distributed-systems rigor (idempotency, transactions, async queues, monitoring) and builds secure, versioned FastAPI APIs with JWT/OAuth2, RBAC, and database row-level security.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built and owned a customer-facing Document Intelligence Service for legal contract analytics at Noasis Digital, delivering extraction/summarization with careful accuracy controls (confidence thresholds, versioned deployments, production logging). Also developed a React/TypeScript document review app and internal QA dashboard, and has hands-on microservices experience with async messaging (RabbitMQ), timeout tuning, and centralized structured logging for reliability at scale.”

“Built and deployed production LLM applications including a natural-language-to-read-only-SQL system focused on ambiguity handling and query safety (schema whitelisting, intent validation, confidence checks, deterministic execution). Experienced with LangChain-based, modular agent orchestration and RAG document QA for large PDFs, with a metrics-driven testing/evaluation approach and cross-functional delivery with marketing on an AI content recommendation/search tool.”

“Lifecycle/CRM marketer from Lid Vizion who led behavior-segmented onboarding/reactivation email programs, delivering a 26% lift in email engagement. Experienced running A/B tests (subject lines, CTAs, send times) and coordinating cross-functionally across creative/product/dev to align influencer, blog, and email messaging—cutting go-to-market time by 20%.”

Riya Stanly

Screened

Intern Data Scientist specializing in machine learning, NLP, and LLM fine-tuning

2y exp

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”

Charmi Dharmendrakumar Parmar

Screened

Mid-Level Software Engineer specializing in full-stack and cloud-native systems

Alpharetta, GA4y exp

“Backend/full-stack engineer who owned a cloud-native, AWS-based microservices backend for an HRIS product used by ~10,000 users, including onboarding and workflow orchestration. Strong production focus on event-driven architecture, idempotency/retries, observability, and developer-friendly API design (OpenAPI, versioning, JWT), plus hands-on Selenium automation for resilient checkout-style flows.”

“Analytics professional focused on procurement, supplier, and contract spend analytics, with hands-on experience using Snowflake SQL, Python, and Power BI to turn messy financial and vendor data into decision-ready reporting. They appear strongest in building reusable data workflows, defining business KPIs with finance/procurement partners, and validating results through reconciliation and stakeholder review.”

“Salesforce-focused engineer with hands-on ownership of complex Sales Cloud and Service Cloud solutions, including automated lead-to-opportunity lifecycle workflows, LWC-based lead management UIs, and Aura case management consoles. Stands out for making thoughtful architecture tradeoffs between Flow and Apex, with strong attention to scalability, governor limits, UX performance, and post-release monitoring.”