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Vetted Model Fine-tuning Professionals
Pre-screened and vetted.
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).”
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
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 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
Junior Machine Learning Engineer specializing in LLMs, RAG, and fine-tuning
Mid-level Research Assistant specializing in interpretable ML and AI evaluation
Manas Kumar Sherla
Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI
“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”
Junior Machine Learning Engineer specializing in Agentic RAG and Document AI