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Vetted ChromaDB Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech
Mid-level Data Scientist specializing in Generative AI and LLM applications
Senior AI Full-Stack Engineer specializing in GenAI, RAG, and scalable ML systems
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
Mid-Level Software Engineer specializing in Cloud SRE and LLM-powered automation
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Abhinav Bandaru
Junior Data Scientist specializing in Generative AI and agentic LLM systems
“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”
Mid-level AI/ML Engineer specializing in GPU-accelerated LLMs, RAG, and production MLOps
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLMs, RAG, and recommendations
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Intern Machine Learning Engineer specializing in LLMs, RAG, and model quantization
Dilpreet Singh
Executive CTO and Founder specializing in AI platforms and hyper-scale SaaS
“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
kartik kanotra
Mid-level Software Developer specializing in cloud data engineering and MLOps
“Software engineer with strong AWS production experience, including an end-to-end historical backfill system exporting ~10PB of CloudWatch logs into a data lake using Step Functions/Kinesis/Lambda/Firehose/Glue. Emphasizes reliability and operability (DynamoDB checkpointing, monitoring dashboards, CI/CD with canary tests) and has also built customer-facing UI work for the Visa Developer Portal using Angular + Spring Boot, plus React/Redux frontend work.”
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Kenneth Young
Senior Site Reliability Engineer specializing in production LLM/RAG deployments
“Built and operationalized an internal LLM/RAG system for engineering specs—starting with an at-home prototype using real ERP documents, then securing hardware, standing up a GPU/software stack, and deploying through UAT to production. Identified organizational gaps (no shared spec repository) and created a queryable RAG database that reportedly cut document discovery from days/weeks to minutes, while also resolving retrieval issues via improved PDF-aware chunking.”
YAKKALI PAVAN
Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems
“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”
Executive Technology Leader (CTO/Principal Engineer) specializing in cloud-native platforms and AI