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Vetted Recommender Systems Professionals
Pre-screened and vetted.
Junior AI/ML Engineer specializing in LLMs, recommender systems, and computer vision
Junior AI/ML Engineer specializing in LLMs, RAG systems, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production inference platforms
Junior AI Engineer specializing in NLP, LLMs, and recommender systems
Mid-level Backend & Blockchain Developer specializing in Web3 and cloud-native systems
Junior Full-Stack Software Engineer specializing in cloud-native healthcare applications
Junior Data & BI Analyst specializing in analytics engineering, NLP, and financial risk analytics
Senior Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack & AI Engineer specializing in FinTech and ML-powered applications
Senior Full-Stack Engineer specializing in AI, computer vision, and real-time systems
Junior AI/ML Engineer specializing in Generative AI production systems
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Senior Software Engineer specializing in backend ML and Generative AI platforms
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Krystal Galdamez
Junior Full-Stack Engineer specializing in AI-powered web applications
“Startup-focused engineer who has shipped Python backend features, AI integrations, and Playwright automation for products including an AI coaching platform and hiring workflow tools. Stands out for working through ambiguous zero-spec environments, hardening flaky Firebase-authenticated test flows, and designing practical fallback paths when AI outputs are unreliable.”
Sana Khan
Mid-Level Software Developer specializing in cloud-native microservices, iOS, and ML deployment
“Backend engineer with production ERP experience deploying microservices and improving performance/reliability using a metrics-driven approach (logs, latency, error rates). Has hands-on cloud/hybrid operations across AWS and Azure with Docker/Kubernetes, and has resolved real-world mobile sync issues by tuning timeouts/retries and reducing payload sizes. Builds configurable Python services to deliver customer-specific behavior without destabilizing the core codebase.”
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.”