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Vetted Vector Search Professionals
Pre-screened and vetted.
Executive Engineering Leader specializing in cloud platforms, data, and generative AI
Mid-level AI Engineer specializing in LLMs, computer vision, and production ML systems
Senior Machine Learning Engineer specializing in Generative AI and Healthcare IT
Director-level AI and data science leader specializing in clinical data and analytics
Senior GenAI Engineer specializing in enterprise LLM systems and RAG platforms
Intern Data/Software Engineer specializing in APIs, LLM pipelines, and full-stack web apps
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Mid-level Full-Stack GenAI/ML Engineer specializing in agentic AI and RAG systems
Mid-level Software Engineer specializing in GenAI, RAG, and distributed systems
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Senior Software Engineer specializing in full-stack web apps and LLM/RAG systems
Mid-level Data Engineer specializing in cloud data platforms and BI analytics
Principal Full-Stack Engineer specializing in cloud-native platforms and AI-powered developer tools
Tushar Gwal
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and MLOps
“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Rui Zhao
Junior Machine Learning Engineer specializing in semantic search and retrieval systems
“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”