.png){kind=logo}
Vetted Prompt Engineering Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
Mid-level Full-Stack Software Engineer specializing in AI, data pipelines, and cloud-native apps
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Junior Data Systems Analyst specializing in ML, NLP, and cloud deployment
Junior ML Engineer specializing in search, retrieval, and recommendation systems
Junior AI/ML Engineer specializing in LLMs, automation, and backend data pipelines
Mid-level Web Developer specializing in WordPress, React, and AI evaluation
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Junior Generative AI Engineer specializing in LLM systems and RAG
Mid-level Generative AI Engineer specializing in LLMs, RAG, and prompt engineering
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
Mid-Level Software Engineer specializing in distributed microservices and cloud-native systems
Mid-level AI/ML Research Engineer specializing in NLP, LLM agents, and multimodal systems
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Junior AI Engineer specializing in production RAG systems and GPU-accelerated inference
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
āBuilt and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%ā90% / +20%; latency ~2.0sā1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).ā