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Vetted Vector Databases Professionals
Pre-screened and vetted.
Senior ETL/Data Engineer specializing in cloud data platforms and AI/ML-ready pipelines
Mid-level Data Engineer specializing in cloud lakehouse and streaming platforms
Mid-level GenAI/ML Engineer specializing in LLM agents, RAG, and document intelligence
Mid-level AI/ML Engineer specializing in Generative AI agents and enterprise analytics
Senior AI/ML Engineer specializing in computer vision, GenAI, and 3D spatial analytics
Senior Machine Learning Engineer specializing in AI, NLP, computer vision, and GenAI
Mid-level Software Engineer specializing in full-stack FinTech and AI systems
Senior AI/ML Engineer specializing in Generative AI and conversational systems
Junior Full-Stack Engineer specializing in AI agents, RAG, and distributed systems
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI
Mid-level GenAI/ML Engineer specializing in LLMs, RAG, and agentic AI
Executive engineering leader specializing in AI, FinTech, and cloud platforms
Rafael Ortega
Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems
“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”
Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”
Nithin Reddy
Mid-Level Software Development Engineer specializing in distributed systems and event-driven architectures
“Built and maintained an internal JavaScript/React real-time event monitoring UI used by multiple Goldman Sachs teams (e.g., Private Wealth Management and Bulk Trading Systems). Focused on scaling performance under hundreds of events/sec—using profiling, memoization, batching, and debouncing—and paired it with strong internal documentation and disciplined incident diagnosis via synthetic load testing and logs/metrics.”