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Vetted FAISS Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Machine Learning Engineer specializing in LLMs and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Senior Research Scientist specializing in LLM verification and fraud/risk modeling
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
Senior Full-Stack Developer specializing in cloud-native microservices and AI-driven healthcare apps
Senior Full-Stack Python Engineer specializing in cloud microservices and AI/LLM systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”
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).”
Sason Torosean
Principal Data Scientist specializing in financial risk, forecasting, and applied ML
“ML/NLP practitioner and technical founder who built an AUP risk-scoring model at Bill.com using TF-IDF + SVD features with XGBoost, and previously created automated data-quality guardrails for a Global Equity Risk stacked ML model at Thomson Reuters. Recently built a RAG-based chatbot for PaymentJock’s Home Affordability Probability product using embeddings and a local vector database (FAISS/Chroma), improving answer quality through chunking rather than expensive fine-tuning.”