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Vetted LoRA Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Pradeep Nadella
Mid-level AI/ML Engineer specializing in LLM optimization and real-time fraud/risk modeling
“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Intern Software Engineer specializing in Machine Learning and Generative AI
Senior AI Full Stack Engineer specializing in generative AI and ML systems
Staff AI Full-Stack Engineer specializing in LLMs, multi-agent systems, and Voice AI
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech
Senior AI/ML Engineer specializing in generative AI and recommendation systems
Mid-level AI/ML Engineer specializing in Generative AI and multilingual NLP
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Deepak Guntreddi
Mid-level Full-Stack Developer specializing in Java/Spring Boot and React
“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior AI/ML Engineer specializing in foundation models, LLMs, and agentic AI
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps