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Vetted BigQuery Professionals
Pre-screened and vetted.
Principal Data Scientist specializing in Generative AI and MLOps
Senior Solutions Architect specializing in cloud infrastructure, AI/LLM security, and data platforms
Technology Executive specializing in AI-native engineering and cybersecurity governance
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Senior Full-Stack Software Engineer specializing in SaaS, cloud, and AI/LLM applications
Mid-level Full-Stack Python Developer specializing in cloud-native FinTech and GenAI
Mid-level AI/ML Engineer specializing in LLMs, RAG, and enterprise AI
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Senior Software Engineer specializing in distributed systems and cloud-native microservices
Mid-level Business Analyst specializing in data analytics and enterprise reporting
Mid AI/ML Engineer specializing in LLM systems and inference optimization
Pavan Devulapalle
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Sanketh Reddy
Senior Data Engineer specializing in cloud data platforms and large-scale ETL
“Data engineer focused on large-scale ETL/ELT pipelines across cloud stacks (GCP and AWS), including Spark-based transformations and orchestration with Airflow. Has experience loading up to ~2TB per BigQuery target table and designing atomic loads to multiple downstream systems (Elasticsearch + Kafka), with Kubernetes deployment and Jenkins CI/CD.”
Sriram Rajaraman
Senior Infrastructure Platform Architect specializing in Kubernetes and hybrid cloud
“Platform/infra engineer with strong ownership of Kubernetes on VMware and day-to-day hybrid on-prem-to-AWS operations. Has hands-on experience automating infrastructure delivery with Terraform/Ansible/CI-CD, and has resolved real production issues spanning CSI storage reattachment during upgrades, vSphere storage-latency performance degradation, and hybrid connectivity/routing failures with improved validation, monitoring, and failover.”
Abhay Murjani
Director-level Data Science Manager specializing in ML forecasting, experimentation, and MLOps
“Data/ML engineer with experience at American Express and Amazon, owning an end-to-end rewards redemption/liability ML pipeline (~200GB) with rigorous regulatory/audit validation and quarterly executive reporting. Also built web-scraped product datasets with anti-bot protections at a startup and helped modernize an authn/authz service using AWS, plus led early-stage migration work from an internal warehouse to GCP with CI/CD and cloud observability.”
Joseph Rivas
Senior AI/ML Engineer specializing in GenAI, MLOps, and computer vision
“ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.”