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Vetted Llama Professionals
Pre-screened and vetted.
Senior Full-Stack Software Engineer specializing in cloud platforms and AI evaluation
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-Level Software Engineer specializing in cloud-native microservices and AI/ML
Mid-level AI/ML Engineer specializing in MLOps, real-time ML, and LLM/RAG systems
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Senior AI/ML Engineer specializing in Computer Vision, NLP, and Generative AI
Technology Executive specializing in AI-native engineering and cybersecurity governance
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Intern AI Software Engineer specializing in LLM inference optimization and model compression
Senior Data/GenAI Engineer specializing in cloud-native ML, RAG, and real-time data platforms
Junior AI/ML Engineer specializing in LLM agents and full-stack AI systems
Surya Singh
Mid-level AI/ML Engineer specializing in FinTech and fraud detection
“ML/backend engineer with PayPal experience building high-stakes production systems, including a GenAI internal support assistant and a real-time fraud scoring pipeline. Strong in Python/FastAPI, model-serving infrastructure, RAG architecture, and production observability, with clear readiness to transition those backend patterns into a TypeScript stack.”
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”
Jiacheng Yin
Intern software engineer specializing in AI, backend systems, and cloud infrastructure
“Backend/AI systems engineer who has shipped production LLM agents focused on prompt engineering, code generation, and incident-response automation. Stands out for combining strong agent orchestration and reliability engineering with measurable business impact, including 60-70% cost reductions, 45% lower monthly LLM spend, and a 5x increase in developer iteration speed.”
Sairaghav Nissankula
Senior AI/ML Engineer specializing in LLMs, NLP, and enterprise conversational AI
“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”
Joseph Rivas
Senior AI/ML Engineer specializing in GenAI, MLOps, and computer vision
“ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.”
Pranav Puranik
Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP
“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”