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Vetted GPT Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Staff AI/ML Engineer specializing in LLMs, fraud detection, and MLOps
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.”
Senior Full-Stack Software Engineer specializing in cloud automation and applied AI
Senior Full-Stack Software Engineer specializing in AI voice agents and scalable web platforms
Software Development Manager specializing in multimodal visual search and AI platforms
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Python Engineer specializing in cloud infrastructure, media services, and IoT
Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech
Principal Data Scientist specializing in Generative AI and security analytics
Senior AI/ML Engineer specializing in generative AI and recommendation systems
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.”
Vishanth Iyer
Senior AI/ML Engineer specializing in LLMs, multimodal AI, and scalable MLOps
“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”
Executive AI/IoT Engineering Leader specializing in full-stack and edge AI systems
Senior Data Scientist / ML Engineer specializing in LLMs, generative AI, and MLOps
Mid-level AI/ML Engineer specializing in LLM infrastructure and FinTech ML platforms
Senior Full-Stack Developer specializing in cloud-native microservices and AI-driven healthcare apps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Subhash Mothukuru
Mid-level Machine Learning Engineer specializing in LLMs, generative AI, and MLOps
“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”