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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Senior AI/ML Engineer specializing in Generative AI, LLMs, and data platforms
Senior AI/ML Engineer specializing in NLP, computer vision, and cloud ML systems
Mid-level MLOps/Machine Learning Engineer specializing in cloud-native production ML
Junior AI/ML Engineer specializing in LLMs, RAG systems, and MLOps
Senior AI/ML Engineer specializing in Generative AI, RAG, and multimodal LLM systems
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Senior Software Engineer specializing in cloud-native microservices and AI/ML automation
Senior Full-Stack & AI Engineer specializing in FinTech and Healthcare
Mid-Level ML/AI Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Junior Software Engineer specializing in ML inference infrastructure
Senior Full-Stack Software Engineer specializing in AI/LLM-powered web applications
Senior Full-Stack Python Engineer specializing in AI/LLM-powered web applications
Mid-level AI Engineer specializing in agentic LLM workflows and RAG systems
Jatin Soni
Mid-level Software Engineer specializing in Generative AI and scalable backend systems
“Backend/AI engineer with production experience in legal tech: built a high-scale licensing/subscription API (FastAPI/Postgres/Stripe) and shipped a RAG-based chatbot for an eDiscovery platform. Designed a robust legal document ingestion workflow that processes thousands of documents into a searchable vector index with clear retry/escalation logic, and has demonstrated measurable Postgres performance wins (200ms to 10ms) using EXPLAIN ANALYZE and composite indexing.”
Balakrishna Mylapilli
Mid-level AIML Engineer specializing in production ML and MLOps
“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”