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Vetted GPT-4 Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in AI, cloud microservices, and blockchain
Mid-level Technical Product Manager and R&D Software Engineer specializing in AI products
Senior AI/ML Engineer specializing in Generative AI, NLP, and LLM systems
Senior Frontend Engineer specializing in scalable web platforms
Junior Software Engineer specializing in ML, computer vision, and data engineering
Mid-Level Software Engineer specializing in cloud-native microservices and AI/ML
Mid-level AI/ML Engineer specializing in MLOps, real-time ML, and LLM/RAG systems
Principal Data Scientist specializing in Generative AI and MLOps
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Surya Singh
Mid-level AI/ML Engineer specializing in FinTech and fraud detection
“ML/backend engineer with PayPal experience building high-stakes production systems, including a GenAI internal support assistant and a real-time fraud scoring pipeline. Strong in Python/FastAPI, model-serving infrastructure, RAG architecture, and production observability, with clear readiness to transition those backend patterns into a TypeScript stack.”
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.”
Poorna Pedapudi
Mid-Level Software Engineer specializing in distributed backend systems and cloud-native microservices
“Software engineer focused on data platforms and applied LLM systems: built an internal data quality monitoring layer to catch silent data drift and iterated post-launch after finding ~30% false-positive alerts, reducing noise via dynamic baselines and improved structured logging. Also shipped a production RAG-based internal knowledge assistant over Jira/Confluence with citations, confidence-based fallbacks, and nightly automated evals to prevent regressions.”
Sairaghav Nissankula
Senior AI/ML Engineer specializing in LLMs, NLP, and enterprise conversational AI
“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”
Harsh Sanas
Intern Full-Stack Engineer specializing in AI and distributed systems
“Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.”
Shubham Singh
Mid-level Software Engineer specializing in LLM systems and intelligent search
“Backend engineer from Palantir who built and productionized an enterprise LLM-based document intelligence/search platform, evolving it into a hybrid lexical+vector retrieval system. Emphasizes reliability and cost control via strict LLM gating, robust fallback paths, and evaluation frameworks (e.g., MMLU/BLEU), plus disciplined migration practices (feature flags, dual-writes, shadow reads) to ship changes safely at scale.”