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Vetted Batch Processing Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in backend microservices and distributed systems
Mid-level Software Engineer specializing in backend microservices and cloud platforms
Mid-level ML Engineer specializing in MLOps and computational biology
Senior Full-Stack Engineer specializing in Python backends and cloud-native systems
Mid-Level Full-Stack Software Engineer specializing in SaaS, FinTech, and LLM applications
Mid-level Data Scientist specializing in Healthcare ML and Generative AI
Mid-level Software Engineer specializing in Java/Python microservices and cloud platforms
Senior Software Engineer specializing in LLM agents and RAG pipelines
Mid-level Full-Stack Developer specializing in Spring Boot microservices and React
Mid-level ML Engineer specializing in FinTech risk, fraud, and GenAI RAG systems
Mid-level Backend/Full-Stack Software Engineer specializing in APIs, data systems, and AWS
Mid-level Data Scientist specializing in Healthcare ML and Generative AI
Mid-Level Software Engineer specializing in backend APIs, cloud microservices, and LLM integration
Mid-level Data Engineer specializing in cloud data pipelines for Healthcare and FinTech
Mid-level AI & Data Engineer specializing in RAG and analytics platforms
Mid-Level Full-Stack Software Engineer specializing in edtech and adaptive learning systems
Mid-Level Backend Software Engineer specializing in AWS microservices and AI/automation
Junior Software Engineer specializing in distributed systems, cloud, and LLM-powered search
Mid-level Sales Development Representative specializing in high-velocity outbound pipeline generation
DhanushKautilya Kammaripalle
Junior AI Integration Engineer specializing in LLM agents and RAG on cloud platforms
“Built and deployed LLM-powered features for a startup organizational management application, focusing on real-world deployment constraints like latency and cost. Implemented RAG with FAISS and improved retrieval quality by switching embedding models (OpenAI/Hugging Face) and fine-tuning embeddings on medical corpora for a medical-report UI feature. Uses LangChain and LangGraph to orchestrate multi-node LLM API workflows and evaluates systems with metrics like latency, cost per request, and error taxonomy.”