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Vetted Semantic Search Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time AI systems
Junior Full-Stack Software Engineer specializing in cloud-native e-commerce and AI search
Mid-level Data Scientist / AI/ML Engineer specializing in secure cloud ML and GenAI
Mid-level Data Scientist specializing in LLMs and NLP for financial analytics
Mid-level Full-Stack Engineer specializing in FinTech and real-time distributed systems
Junior Full-Stack & ML Engineer specializing in AI products and real-time systems
Senior AI and Full-Stack Engineer specializing in LLM-powered microservices
Senior Software Engineer specializing in GenAI and full-stack enterprise applications
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Mid-level Software Engineer specializing in full-stack AI and cloud-native systems
Senior Full-Stack Engineer specializing in compliance, integrations, and data platforms
Senior AI Architect specializing in Generative AI and LLM systems
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Swaraj Bhalerao
Mid-level Full-Stack Developer specializing in backend-heavy web applications
“Backend/full-stack engineer who has built AI-powered search and workflow systems in production, including a semantic resume-matching platform for recruiters and internal security data dashboards at ReliaQuest. Stands out for combining modern AI tooling with pragmatic reliability, performance tuning, and strong product intuition in ambiguous environments.”
Bhavana G
Mid-level GenAI Engineer specializing in AI agents and RAG systems
“Built and deployed a production LLM-based RAG agent platform adopted by multiple business teams (Marketing, GTM, Recruiting, Customer Support) to automate knowledge search, Q&A, and content generation. Emphasizes production-grade reliability (grounding/validation/guardrails), rigorous evaluation/monitoring, and cost-aware scaling via model tiering, prompt/retrieval optimization, and caching using LangChain/LangGraph orchestration.”