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Vetted Vector Databases Professionals
Pre-screened and vetted.
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
Senior Data Engineer specializing in multi-cloud lakehouse architectures and privacy/AI governance
Senior Machine Learning Engineer specializing in Generative AI and LLM systems
Senior AI/ML Engineer specializing in LLM, NLP, and production ML systems
Mid-level Software Engineer specializing in full-stack AI and cloud-native systems
Mid-level Full-Stack Developer specializing in Python, microservices, and AI platforms
Mid-level Frontend Engineer specializing in React and web applications
Mid-level AI Engineer specializing in production LLM, RAG, and agentic AI systems
Senior AI Python Engineer specializing in Generative AI and MLOps
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.”
Saikrishna Paila
Junior AI Engineer specializing in RAG pipelines and agentic AI systems
“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”
Pravallika Kilari
Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance
“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”
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.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
“Built an LLM-powered academic research assistant for a professor (LangChain + OpenAI + arXiv) focused on synthesizing papers quickly, with emphasis on reliability (ReAct prompting, citation verification) and cost control (caching). Has production MLOps/orchestration experience at Cisco and HCL Tech using Kubernetes, plus MLflow and GitHub Actions for lifecycle management and CI/CD.”