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Vetted PyTorch Professionals
Pre-screened and vetted.
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
Intern Software Engineer specializing in cloud infrastructure, DevOps, and AI-enabled systems
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Entry-Level Full-Stack Engineer specializing in backend APIs and cloud architectures
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
Junior Full-Stack Developer specializing in AI integrations and LLM research
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 Full-Stack Software Engineer specializing in AI automation and RAG agents
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
Entry-level Full-Stack Engineer specializing in AI-powered applications
Mid-level Full-Stack AI Engineer specializing in agentic AI and RAG systems
Entry-level AI/ML Engineer specializing in RAG and conversational AI
Haeshitha Indukuri
Entry AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Built 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%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”
“Full-stack AI engineer who has built and deployed multiple end-to-end LLM products, including an AI interview assistant, a multi-agent market research platform, and a policy document explainer. Particularly strong in productionizing agentic workflows, integrating tools like Whisper, Tavus, LiveKit, CrewAI, and LangGraph, and hardening messy real-world AI/document pipelines with validation, memory isolation, and fallback handling.”