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Vetted Model Fine-tuning Professionals
Pre-screened and vetted.
Director-level Applied ML Engineer specializing in GenAI, LLM systems, and MLOps
Senior Software Engineer specializing in AI/ML and cloud backend systems
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 Full-Stack Software Engineer specializing in cloud, distributed systems, and AI
Prasad Sadineni
Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems
“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”
Shehab mohamed mohamed
Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems
“ML/AI engineer focused on production-grade systems, with experience building a low-latency multi-agent 'neural concierge' booking platform used across domains like restaurants and hospitals. Also worked on a healthcare computer vision system for nystagmus/eye-movement analysis, showing a mix of scalable LLM infrastructure, MLOps, and safety-conscious medical AI experience.”
Karthikeya Arra
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Junior Machine Learning Engineer specializing in LLMs and multimodal AI
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 Generative AI Engineer specializing in LLM systems and RAG
Mid-level Java Full-Stack Developer specializing in cloud microservices and AI/ML integration
Mid-level AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
Mid-level AI/ML Engineer specializing in GenAI, agentic AI, and RAG pipelines
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).”
Mid-level Generative AI & ML Engineer specializing in LLMs, RAG, and MLOps
Kealan Doherty
Junior Software Engineer specializing in full-stack web and AI applications
“Early-career backend developer building an Arc Raiders game-data API end-to-end in Go on AWS with PostgreSQL, making their first real deployment and learning system design hands-on. Also built an AI resume and cover letter generator using Gemini and has experience debugging open-source code and managing PRs in a capstone team project.”