.png){kind=logo}
Vetted ChromaDB Professionals
Pre-screened and vetted.
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”
Pavan Sai Reddy Pendry
Junior AI/ML Engineer specializing in GenAI, RAG, and full-stack ML systems
“Built a university campus assistant chatbot (BabyJ/WWJ) using RAG and agentic routing with a FastAPI + React stack and JWT auth, focusing heavily on production concerns like latency and reliability. Uses techniques like speculative prefetching, smart intent routing, and rigorous eval/testing (golden sets, regression, edge cases) while collaborating closely with campus admin/advising teams to iterate based on real user feedback.”
Mid-level Data Scientist / AI Research Engineer specializing in LLMs, RAG, and applied ML
Junior Software Engineer specializing in full-stack development and applied AI
Mid-level AI/ML Engineer specializing in LLM-powered RAG systems and MLOps
Junior AI/ML Engineer specializing in LLMs, RAG, and applied NLP
Junior Full-Stack Software Engineer specializing in backend APIs and data pipelines
Junior Backend Software Engineer specializing in APIs, databases, and AI applications
Junior AI/LLM Engineer specializing in voice agents, RAG, and robotics systems
Mid-level Backend Software Engineer specializing in distributed systems and AI platforms
Senior Full-Stack Engineer specializing in Python/TypeScript web apps and AI (RAG, agentic workflows)
Junior Full-Stack Developer specializing in JavaScript, React, and MongoDB
Junior Generative AI Engineer specializing in LLM systems and RAG
Junior AI Engineer specializing in production RAG systems and GPU-accelerated inference
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise compliance & fraud systems
“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”