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Vetted Vector Search Professionals
Pre-screened and vetted.
Mid-level Full-Stack Engineer specializing in AI, LLM/RAG platforms, and data systems
Senior Data Engineer specializing in Azure Lakehouse and LLM/ML data platforms
Mid-level Full-Stack Developer specializing in AI platforms and enterprise web applications
Senior AI/ML Engineer specializing in conversational AI and LLM systems
Mid-level Software Engineer specializing in cloud-native backend and FinTech systems
Senior Backend Software Engineer specializing in Python, cloud microservices, and FinTech
Senior Software Engineer specializing in cloud-native SaaS, IoT, and GenAI platforms
Junior Full-Stack Software Engineer specializing in cloud-native e-commerce and AI search
Mid-level Data Scientist specializing in LLMs and NLP for financial analytics
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and computer vision
Junior Full-Stack & ML Engineer specializing in AI products and real-time systems
Mid-Level Full-Stack Software Engineer specializing in AI-native workflows
Senior AI and Full-Stack Engineer specializing in LLM-powered microservices
Aniruddha Rajnekar
Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems
“Full-stack engineer with about 3 years of experience who is deeply hands-on with AI-assisted development and agentic systems. Built TubeAgent using LangChain, Ollama, FAISS, and Llama 3, and has demonstrated measurable impact by cutting review time by 90% and reducing deployment time from 30 minutes to under 5 minutes at NC State. Combines practical experimentation with strong architectural thinking around resilient, composable AI systems.”
Ruudra Patel
Junior Data Scientist specializing in ML, LLMs, and RAG applications
“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”
Madhu Sriram Sengottu Velan
Entry-Level Software Engineer specializing in backend systems and distributed services
“Backend/AI engineer from an early-stage Japan-based startup (WorkAI) who built a multi-tenant RAG system integrating Notion/Slack/Google Drive with Pinecone and OpenAI, including a chatbot retrieval workflow. Experienced in production reliability (rate limits, retries, verification layers), strong Python/FastAPI engineering practices, and PostgreSQL performance optimization; currently based in India and needs sponsorship.”
Fernando Mosqueda
Senior AI/ML Engineer specializing in healthcare AI and MLOps
“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”
Raghvesh Prasad
Senior Full-Stack Engineer specializing in AI, backend systems, and supply chain platforms
“Full-stack engineer with hands-on experience spanning React/TypeScript frontends, Cloudflare serverless RAG systems, SQL-heavy backend redesigns, and computer vision workflows. He has shipped practical automation and reliability improvements with measurable impact, including cutting a video-validation reporting process from a week to 2 days and fixing a memory-heavy shipment system before Black Friday to support 30K+ orders successfully.”
Alfred Fox
Senior AI/ML & Full-Stack Engineer specializing in GenAI, RAG, and MLOps platforms
“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”