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Vetted Snowflake Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in healthcare LLMs and conversational AI
Senior AI/ML Engineer specializing in healthcare and fintech AI systems
Senior Full-Stack Engineer specializing in cloud-native enterprise and AI platforms
Mid AI/Machine Learning Engineer specializing in LLMs, NLP, and Computer Vision
Staff Product Leader specializing in AI, data platforms, and enterprise analytics
Staff Platform/ML Engineer specializing in agentic AI, RAG, and cloud infrastructure
Senior Full-Stack Python Developer specializing in cloud, data platforms, and GenAI
Staff AI & Data Engineer specializing in LLM systems and real-time data platforms
Senior Software Engineer specializing in cloud, data platforms, and LLM/RAG applications
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Mid-level Full-Stack Software Engineer specializing in cloud-native and data platforms
Executive Engineering Leader (CTO/VP) specializing in platform scaling and video streaming
Vinosh Mathuranayagam
Senior solutions architect specializing in digital health and healthcare IT
“Healthcare solutions architect with roughly 7 years at Mass General Brigham, evolving from application specialist to broad cross-functional architect supporting telehealth, digital health, conversational AI, generative AI, and legal workflow initiatives. Particularly strong at translating complex clinical and business problems into enterprise architectures, integrations, and stakeholder-specific demos across hospital leadership, clinicians, IT, and vendors.”
Arnav Singh
Junior Software Engineer specializing in full-stack web, cloud data, and applied ML
“Backend engineer who evolved the X-Ray gaming analytics platform, leading a zero-downtime MongoDB→AWS DocumentDB migration with dual-write, checksum-based validation, and Kubernetes canary rollouts while maintaining real-time monitoring for millions of concurrent sessions. Strong in FastAPI/Python API scaling and performance tuning (cut latency from ~2s to <150ms and reduced DB load 90%) plus production-grade auth/RLS security patterns (JWT, Supabase Auth, PostgreSQL RLS).”
Pavanika Thotakura
Senior Data Engineer specializing in cloud big data pipelines and real-time streaming
“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”
Bhagya Sunkara
Mid-level Full-Stack Developer specializing in cloud-native backend services and real-time data platforms
“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”
Yeshwanth Sai Pala
Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech
“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”
Yashwanth Reddy
Senior Data Engineer specializing in cloud-native data pipelines and lakehouse platforms
“Data engineer at Microsoft who owned an end-to-end subscription analytics platform processing 7TB+ daily across 40+ pipelines, combining ADF batch ingestion with Kafka/Spark streaming and rigorous Great Expectations quality gates. Built a Fabric-based self-service ingestion platform with CI/CD and observability, plus a Databricks feature store serving near-real-time ML inference with Delta Lake reliability and versioning.”