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Vetted SQL Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in computer vision, NLP, and real-time forecasting
Intern Software Engineer specializing in ML and data pipelines
Senior Software Engineer specializing in cloud platforms for healthcare and e-commerce
Junior Product/UX Designer specializing in AI, mobile apps, and design systems
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Intern Data Scientist specializing in machine learning and trustworthy AI
Intern Machine Learning Engineer specializing in systems, kernels, and GPU computing
Intern Machine Learning Engineer specializing in LLM systems and recommendation/search
Mid-Level Software Engineer specializing in Ads Serving and Machine Learning Systems
Mid-level Management Consultant specializing in banking, financial modeling, and AI enablement
Senior Software Engineer specializing in cloud platforms, data pipelines, and ML
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Senior Data Scientist specializing in large-scale ML systems and recommendations
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
Peter Gluck
Principal Software Engineer specializing in scalable, secure multi-platform systems
“Engineering leader and former CTO/Disney|ABC engineering manager who built white-label mobile app architecture that supported a branded app and successful exit, and later scaled Disney Channel brand app teams across iOS/Android/web and major OTT platforms. Led kid-safety architecture for DisneyNOW (“Junior mode” with PIN) and partnered with product/legal on COPPA compliance, emphasizing maintainable tech choices and strong execution processes.”
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.”
Anirudha Deo
Senior Engineering Manager specializing in cloud-native e-commerce and payments platforms
“Senior Engineering Manager with large-scale platform/API ownership at eBay, leading a globally distributed team and redesigning Order public APIs used by external developer ecosystems at ~800M requests/day, delivering 84% performance gains and reducing compute by ~300 VMs. Also led Google CRES ETL migration work on GCP, creating reusable Python libraries to standardize configuration across 10 integrations and improve developer productivity.”
Akhil Maram
Senior Data Engineer specializing in real-time data platforms and lakehouse architectures
“Senior, product-focused engineer who has built real-time customer-facing web applications and a microservices backend (TypeScript/React/Node) using RabbitMQ, MongoDB, and Redis. Demonstrates strong operational maturity (idempotency, tracing/observability, backpressure) and built an internal console that became the primary tool for debugging, replaying jobs, and managing system behavior.”
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.”
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.”