Vetted Snowflake Professionals

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Runs two lean real estate companies remotely by building local on-the-ground contact networks and leveraging free-tier technology to keep total annual business costs under $100. Brings a cost-elimination and MVP/validation-first mindset, preferring to join an established company unless a clearly viable business idea emerges.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.”

“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”

“Product-focused full-stack engineer (70/30 app vs infra) with Accenture experience and recent AI workflow work, shipping end-to-end systems from React/TypeScript UIs through FastAPI backends to Postgres. Built an AI-driven data extraction platform with async job APIs, strict schema validation, and strong observability, and has operated AWS ECS-based deployments with real incident mitigation (DB connection exhaustion/latency under traffic spikes).”

“Embodied AI / robotics-focused ML engineer with experience at JPMorgan and EY building language-to-robot control systems that connect transformer/LLM intent to safe real-world robotic actions. Designed production-grade, low-latency architectures (Kafka/Redis, monitoring, CI/CD) and applied sim-to-real and model distillation to make research ideas deployable on physical systems.”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“Technically oriented operator with experience driving a strategic migration to Microsoft Azure to modernize a company toward microservices and CI/CD, improving scalability and positioning for long-term optimization. Evaluates product ideas through an operational lens (efficiency, decision support, process optimization) and emphasizes building viable products with paying customers while maintaining revenue resilience.”

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Backend/data engineer focused on AWS-based, event-driven systems—building Golang microservices and serverless pipelines with strong data validation, observability (CloudWatch/Splunk/New Relic), and reliability patterns (retries/DLQs). Has also operated distributed web scraping/data collection with schema versioning and Step Functions backfills, and ships well-documented, versioned REST/WebSocket APIs for internal and external consumers.”

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

Tuukka Luolamo

Screened

Executive Technology Leader (CTO) specializing in AI, cloud, and distributed platforms

Remote14y exp

“Engineering leader who stays hands-on in high-leverage technical areas (architecture, scalability, reliability) while operating at an executive level. Led StagePilot’s shift from a tightly coupled legacy system to a cloud-native, event-driven real-time platform proven at 1M+ concurrent users, and previously scaled multiple SRE teams at McGraw-Hill with SLOs, on-call, and blameless ops practices.”

Sana Khan

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time inference in FinTech

Oklahoma, USA4y exp

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

Jake Lee

Screened

Junior Full-Stack/Systems Engineer specializing in AI, embedded systems, and healthcare apps

Boston, MA3y exp

“Led architecture for “Solstice/Solstis,” a safety-aware, hands-free AI medical assistant that guides users through minor emergencies with a structured, state-machine-driven LLM agent integrated with device hardware. Built RAG grounded in Red Cross procedures plus guardrails, fallbacks, and emergency escalation, and improved real-world usability by shifting from open-ended chat to a deterministic step-by-step workflow measured via completion rate, repeat prompts, and latency.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

Abdul Mohammed

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

USA3y exp

“Built and productionized an LLM-powered clinical documentation and insights pipeline at Cardinal Health using LangChain + GPT-4 with RAG to summarize long clinical notes, extract medication/dosage entities, and generate structured SQL-ready outputs for downstream analytics. Emphasizes clinical reliability via labeled benchmarking (precision/recall/F1), shadow deployments, clinician human-in-the-loop review, and ongoing monitoring/orchestration with Airflow, Lambda, S3, Postgres, and Power BI.”

Mukul Sai Pendem

Screened

Mid-level Full-Stack Engineer specializing in cloud-native microservices and DevOps

United States (Remote)4y exp

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

“Data engineer with experience at Capital One and HSBC building and operating GCP-based data platforms. Led an end-to-end Oracle-to-BigQuery migration processing ~200–300GB/day using Dataflow/Beam, Airflow, Dataproc/PySpark, and Looker, achieving ~99.5% pipeline success and ~30% fewer data quality issues. Strong in production reliability, schema drift handling for external APIs, and BigQuery performance/serving patterns (materialized views, authorized views, versioned datasets).”

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

Sukesh Anamaneni

Screened

Senior Business Analyst specializing in AI and commercial banking analytics

Detroit, MI5y exp

“Analytics candidate with hands-on experience supporting a workforce system transformation from symplr to Oracle Fusion Time and Labor, using SQL and Python to turn operational HR, attendance, and payroll data into reporting-ready datasets. They emphasize performance optimization, reusable analytics pipelines, and metric consistency across dashboards, with project work focused on overtime reduction, workforce efficiency, and retention trends by department.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”