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Vetted MLOps Professionals
Pre-screened and vetted.
Staff-level Data Scientist specializing in LLMs, NLP, and product experimentation
Mid-level Machine Learning Engineer specializing in Generative AI and AI for science
Executive Platform & Infrastructure Engineering Leader specializing in FinTech SaaS, Cloud, Data & AI
Mid-level Machine Learning Engineer specializing in LLMs, ranking, and scalable ML systems
Executive Technology Leader specializing in Cloud, Data Platforms, and AI/ML
Mid-level Data Engineer specializing in AI/ML and cloud data platforms
Staff Software Engineer specializing in AI, distributed systems, and data infrastructure
Intern Robotics Software Engineer specializing in perception and ML systems
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Intern Machine Learning Engineer specializing in LLM systems and recommendation/search
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Senior Data Scientist specializing in large-scale ML systems and recommendations
Senior Software Engineer specializing in data platforms, APIs, and healthcare systems
Brian Sanders
Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines
“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”
Daniel Ram
Staff/Director-level Simulation & Computational Geometry Leader specializing in GPU physics and GCP
“Entrepreneurial builder and former founding CTO of a Y Combinator startup that was acquired. Now in the planning stage for a new venture (considering edtech, media creation, and food/"intelligent servicing"), emphasizing early design partners, rapid testing, and iteration to validate ideas.”
Riccardo Bernardi
Senior Infrastructure Engineer specializing in cloud, Kubernetes, and MLOps
“LLMOps-focused technical leader who took an LLM use case from prototype to production for a non-technical customer by combining trust-building and structured enablement with a robust AWS/Kubernetes-based MLOps stack. Built observability and rollback mechanisms (Grafana + MLflow) to troubleshoot in real time, and scaled delivery by hiring a 5-person team while partnering with sales to manage expectations and drive adoption across departments.”
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.”
Ravikanth Kasamsetty
Executive engineering leader specializing in AI, data platforms, and cloud SaaS
“Senior engineering executive with a rare mix of VP-level people leadership and deep hands-on architecture across cloud-native and AI platforms. He led secure hybrid SaaS design at Promethium, built GenAI-powered analytics with LLMs/RAG, and translated customer needs into product wins including the largest deal in company history and a PLG motion that generated 100+ leads.”
Kishore Velchuri
Executive platform engineering leader specializing in multi-cloud SaaS and data platforms
“Senior engineering leader with a rare blend of deep platform/infrastructure expertise and large-scale people leadership, spanning hands-on cloud-native architecture through management of globally distributed organizations of up to roughly 180 engineers. Particularly strong in Kubernetes platform modernization, reliability engineering, and scaling high-throughput SaaS systems while aligning platform investments to customer and business outcomes.”