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Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in Generative AI agents and FinTech risk systems
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Staff Machine Learning Engineer specializing in LLMs and cloud-native AI platforms
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Junior Data Analyst specializing in experimentation, data quality, and ML analytics
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Harish Kasu
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”
Vinay Ramrupe
Mid AI/ML Engineer specializing in LLM and enterprise generative AI
“ML/AI engineer focused on taking LLM systems from experimentation to reliable production, including enterprise copilot and RAG-based knowledge retrieval use cases. Stands out for combining data pipelines, model training, inference optimization, automated evaluation, and safety guardrails, with cited impact including 20% throughput gains and 30% less manual evaluation effort.”
Mid-level Data Scientist specializing in ML for healthcare and strategy analytics
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
Mid-level Machine Learning Engineer specializing in generative AI, NLP, and MLOps
Senior AI Engineer specializing in machine learning, NLP, and generative AI
Sai supriya
Mid-level AI/ML Engineer specializing in LLM alignment, safety, and scalable inference
“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”
Krishna Reddy
Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants
“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”
Gabrielle Burns
Senior AI/ML Engineer specializing in computer vision, NLP, and enterprise ML systems
“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems