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Vetted Transformers Professionals
Pre-screened and vetted.
Senior Machine Learning Engineer specializing in recommender systems, search, and NLP/GenAI
Staff Machine Learning Engineer specializing in LLMs and Generative AI
Principal Data Scientist & AI/ML Engineer specializing in LLMs, recommender systems, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Data Scientist specializing in machine learning, deep learning, and big data
Executive AI/ML Engineer specializing in LLMs, NLP, and production ML systems
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Sudheer Bommichetty
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”
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
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
Senior AI/ML Engineer specializing in recommender systems, GenAI, and applied ML
Senior Machine Learning Engineer specializing in on-device AI and large-scale deep learning systems
Mid-level Data Scientist specializing in Generative AI and LLM applications
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 Data Scientist / ML Engineer specializing in LLMs, generative AI, and MLOps
Senior Software Engineer specializing in e-commerce payments and distributed systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference