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Vetted Llama Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in cloud-native microservices and AI/ML
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
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.”
Rojin Bakhti
Junior Software Engineer specializing in Edge AI and ML deployment
“Qualcomm engineer building Android applications that run on Qualcomm AI accelerators, with hands-on experience in C++ concurrency, chipset stress testing, and power/performance tuning. Has deployed on-device AI models and built deployment/log post-processing workflows using Docker/Kubernetes and CI/CD; interested in translating this embedded AI/performance background into robotics (perception/real-time systems).”
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.”
Junior Software Development Engineer specializing in web UI and AI-enabled experiences
Mid-level AI/ML Engineer specializing in LLMs, agentic systems, and MLOps
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Principal AI Platform Architect specializing in agentic AI and enterprise LLM infrastructure
Senior Content Designer & UX Writer specializing in AI-powered and conversational products
Mid-level Generative AI Engineer specializing in LLMs, NLP, and multimodal systems
Rohith Sadanala
Mid-level Machine Learning Engineer specializing in Generative AI and MLOps
“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”
Nandini Kosgi
Mid-level AI/ML Engineer specializing in LLMs, RAG, and fraud/risk analytics in Financial Services
“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”