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Vetted Recurrent Neural Networks (RNN) Professionals
Pre-screened and vetted.
Principal Data Scientist & AI/ML Engineer specializing in LLMs, recommender systems, and MLOps
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Staff AI/ML Engineer specializing in LLMs, fraud detection, and MLOps
Barbara Skrzypek
Senior Research Scientist specializing in physics, machine learning, and scientific computing
“Research-oriented ML engineer/scientist with deep experience applying generative models, adaptive optimization, and HPC infrastructure to complex physics analyses. Built reusable Python-based tools that replaced expensive Monte Carlo workflows, integrated across HTCondor/SLURM environments, and reduced analysis timelines by 2x while supporting broader team adoption and training.”
Senior AI/ML Engineer specializing in recommender systems, GenAI, and applied ML
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Machine Learning Engineer specializing in ranking, recommendation, and generative AI
Brian Gomez
Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision
“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”
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
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and multi-agent systems
Senior Applied ML Scientist specializing in LLMs, ads ranking, and RAG systems
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Parth Vadera
Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps
“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”
Dennis Noto
Executive Technology & Security Leader specializing in FinTech, AI platforms, and enterprise modernization
“Technology transformation leader who builds board-approved roadmaps and scales engineering orgs with strong Agile execution. Led large modernization efforts (e.g., Scottrade: 3,000 programs/4M LOC in 18 months) and scaled POCs into enterprise SaaS platforms using Docker, Kubernetes, Helm, and Terraform for high-concurrency workloads.”
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.”