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Vetted Vector Databases Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior Data Scientist specializing in Generative AI/NLP for legal and healthcare domains
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Senior Full-Stack Software Engineer specializing in SaaS, cloud, and AI/LLM applications
Junior Software Engineer specializing in cloud platforms, microservices, and AI/ML
Mid-level AI/ML Engineer specializing in NLP, transformers, and RAG systems
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
Junior AI/ML Engineer specializing in agentic AI and cloud optimization
Mid-level Software Development Engineer specializing in backend systems and ML platforms
Senior Full-Stack Engineer specializing in AI automation and LLM-powered products
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Mid AI/ML Engineer specializing in LLM systems and inference optimization
Peeyush Dyavarashetty
Intern AI/ML Engineer specializing in GenAI, LLMs, and agentic RAG systems
“AI/LLM practitioner who built a GPT-2-like language model from scratch at the University of Maryland using PyTorch and multi-GPU distributed training, with experiment tracking in Weights & Biases. As an AI Operations intern at ScaleUp360, delivered multiple production-style AI agent automations (Gmail classification and Fireflies-to-Claude workflows that extract and assign CEO tasks) and set up measurable evaluation using test cases and classification metrics.”
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.”
Vignesh Shanmugasundaram
Junior Software Engineer specializing in full-stack development and applied ML
“Full-stack engineer with experience at Zoho and Amazon who has owned production systems end-to-end, including a monolith-to-microservices migration using Kafka and Cassandra that improved search latency ~25% and increased throughput without data loss. Also built a hackathon project (Buildwise) into a sold product for a construction company (AI-driven document compliance checks) and shipped an IoT-based parking availability MVP in 3 weeks.”
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.”
Xinyuan Lin
Intern Software Engineer specializing in LLMs, RAG, and full-stack systems
“Built and productionized a multi-agent LLM analytics assistant at eBay that routes natural-language questions to retrieval or text-to-SQL, dynamically retrieves relevant schemas via a vector DB, and executes against a data warehouse. Drove a major quality lift (text-to-SQL accuracy 60%→85%) and materially reduced time engineers/PMs spent getting data insights through strong eval/monitoring, tracing, and reliability-focused design (schema retrieval, strict JSON outputs, retries/clarifications).”