Vetted Google Cloud Platform Professionals

“Co-founded a healthcare AI startup building and deploying software directly with end users, emphasizing rapid shipping, deep user interviews, and workflow-first adoption. Has hands-on production deployment experience on AWS (including diagnosing a silent AWS App Runner failure caused by an ARM vs amd64 Docker build mismatch) and is motivated by customer-facing, travel-heavy roles to keep engineering tightly connected to real-world usage.”

“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”

“Built and deployed a production LLM system that scraped Google Maps menu photos, extracted structured prices via OpenAI, and cross-validated them against website-scraped data to automate data-quality verification at scale (replacing costly manual contractor checks). Demonstrates strong reliability instincts—precision-first prompting, output gating with image-quality metadata, and fuzzy matching/RAG techniques—plus solid orchestration (Dagster/Airflow) and observability (Sentry, Prometheus/Grafana).”

“Deloitte engineer who built and shipped AI-powered, Kafka-driven workflow automation for transportation/document processing, including LLM-based semantic search. Strong in production reliability (idempotency, offset management, retries), observability (Datadog/CloudWatch), and database performance tuning (PostgreSQL/Flyway), with measurable latency improvements.”

“Backend engineer on Morgan Stanley’s trade risk and compliance platform, building Java/Spring Boot microservices that validate equity and fixed-income trades at multi-million-events/day scale. Shipped an LLM-assisted trade exception analysis feature using RAG over internal policy documents and trade history, with production-grade guardrails (confidence thresholds, audit logs, human-in-the-loop) and measurable performance wins (~30–35% faster reporting) through PostgreSQL tuning and Redis caching.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Founder of an LLC operating as a consulting firm providing fractional CTO services to startups, giving them parallel exposure to multiple early-stage companies. Has direct experience with MVP development, building org structures from scratch, and supporting early fundraising, and is exploring a pivot from consulting into a scalable product business while staying engaged with the VC/accelerator ecosystem.”

“Azure cloud platform engineer with strong enterprise Linux operations background who designs multi-region HA/DR on Azure (and AWS) using Azure Site Recovery, Traffic Manager, AKS autoscaling, and geo-replicated Azure SQL. Built secure Azure DevOps CI/CD pipelines for .NET/Python microservices to AKS/VMs and provisions full environments via Terraform modules with remote state, drift checks, and staged rollouts; has not directly owned IBM Power/AIX at scale.”

“Application Engineer supporting customers in a financial trading context, experienced in coordinating urgent patch upgrades and stakeholder communications to minimize downtime. Has hands-on production incident response experience troubleshooting a critical dashboard outage caused by a bad SQL execution plan, escalating with DBAs and deploying a fast fix, then documenting full RCA via problem tickets.”

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

“Cofounded a travel app and built a production place recommendation + review system end-to-end using Next.js App Router and TypeScript, including Postgres-backed APIs and post-launch monitoring. Uses structured logging with Sentry and Vercel Analytics to diagnose issues and validate performance improvements, and has some exposure to Temporal-based workflow orchestration with retries/idempotency.”

“Software engineer with experience across TCS, Rakuten, and USC who has owned production integrations and data pipelines end-to-end. Notably improved a trading platform payment flow by replacing fragile polling with a webhook-driven status system with robust fallbacks, and has shipped LLM-assisted design-to-webpage automation plus evaluation-driven prompt iteration (NYT Connections).”

“Cross-functional product/engineering leader with startup and enterprise experience (Whimstay, Wells Fargo, Leap AI Technologies). Has owned end-to-end delivery including UI and payment architecture—built a multi-customer reservation payment flow using pre-auth plus event/timer-driven reminders and handled tricky edge cases like date changeover. Also introduced lightweight process (Jira, sprints, team/module structuring) to increase delivery speed in an early-stage AI document-matching startup.”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

“Data engineer with hands-on experience migrating a legacy/mainframe-fed loader onto GCP, orchestrating daily SFTP-to-GCS ingestion, Spark/Scala transformations, and loading into Cassandra/Solr/OpenSearch with API- and BigQuery-based validation. Also built a Java Spring Boot service that extracts from Hive and produces Excel outputs, emphasizing testing, logging/alerts, and CI setup.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”

Nikitha Kommidi

Screened

Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps

6y exp

“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”

HIMANSHU SHARMA

Screened

Mid-level AI Solutions Engineer specializing in enterprise GenAI and automation

Orlando, FL6y exp

“Built and shipped multiple production LLM/agentic systems, including an agentic RAG NL-to-SQL analytics app that cut manual reporting from 9 hours/week to 15 minutes by grounding on schema-aware retrieval and robust fallback/monitoring. Also implemented a LangChain supervisor-orchestrated enterprise IT automation agent that routes requests for search, identity validation, and action execution, and created a RAG search tool spanning Jira/Confluence/SharePoint for operations stakeholders.”

Kasireddy Kumar reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare, fraud detection, and recommender systems

Missouri, USA6y exp

“Healthcare-focused applied ML/LLM engineer who has deployed production systems including an LLM medical documentation assistant that summarizes unstructured EHR notes into physician-ready structured outputs. Experienced building secure, compliant pipelines (PHI minimization, RBAC, encryption) and scaling via Docker/Kubernetes/Azure ML, plus orchestrating ETL/ML workflows with Airflow and Kubeflow; also built an LLM-driven clinical coding assistant at Centene with measurable performance metrics.”

“Cloud-focused platform/infrastructure engineer centered on AWS and Kubernetes, with hands-on experience building EKS environments via Terraform/Terragrunt, bootstrapping clusters with Cilium and ArgoCD, and integrating persistent storage and backup tooling like EFS/EBS CSI and Velero. While no longer focused on on-prem or hybrid operations, they also bring prior VMware/Hyper-V and migration-to-AWS experience, making them strongest in modern cloud platform engineering roles.”

shivapriya pillalamarri

Screened

Mid-level AI/ML Engineer specializing in financial analytics and production ML systems

Boston, MA4y exp

“Analytics candidate with experience in financial transaction and fraud detection projects, combining SQL data preparation, Python-based automation, and dashboarding. They have owned projects from stakeholder alignment and metric definition through rollout, with emphasis on reducing false positives, improving operational efficiency, and making analytics outputs easy for business teams to adopt.”

“Startup-oriented product/technology leader targeting CTO roles, with experience evaluating and scoping high-impact product expansions. At Crelate, helped assess and shape a contract timekeeping/invoicing initiative that expanded TAM by hundreds of millions and increased ACV 2-3x, contributing to successful market traction and the company’s path to Series C.”

“Built a production LLM-powered diagnostic agent at Supermicro that automated triage of NVIDIA H100/H200 GPU cluster failures by parsing BMC/Redfish logs and recommending fixes from historical RMA data. Their work combined agent architecture, reliability engineering, and backend optimization, delivering a 30% reduction in resolution time and 50% lower database load.”

“Analytics professional with healthcare and operations experience who turns messy enterprise data from platforms like Teradata, GCP, SQL Server, and Snowflake into trusted reporting layers and reproducible analysis workflows. They combine SQL, Python, PySpark, Power BI, and Tableau to improve reporting accuracy and performance, including a 30% dashboard refresh improvement and 20-25% accuracy gains in healthcare reporting.”