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Vetted Anomaly Detection Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in real-time, cloud, and data-driven systems
Executive Platform & Infrastructure Engineering Leader specializing in FinTech SaaS, Cloud, Data & AI
Mid-level Data Engineer specializing in AI/ML and cloud data platforms
Staff Software Engineer specializing in cloud-native AI and supply chain platforms
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level Full-Stack Developer specializing in cloud-native microservices
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Mid-level Software Engineer specializing in real-time backend systems and FinTech payments
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Intern Machine Learning Engineer specializing in systems, kernels, and GPU computing
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Executive founder and COO specializing in AI, industrial automation, and venture growth
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Mid-level Software Engineer specializing in FinTech backend systems
Jeffery Faneuff
Executive Engineering Leader specializing in AI-driven SaaS and IoT platforms
“Engineering leader who built and delivered an IoT smart-spaces platform for the self-storage and smart-living domains, translating customer requirements into architecture, capability maps, and a multi-milestone roadmap. Personally stood up missing AI/ML capabilities (including churn prediction) using Databricks (Delta Lake/MLflow), enabling follow-on features like energy optimization and security/anomaly detection. Scaled an org from 20 to 80+ with disciplined Agile planning (Jira Advanced Roadmaps/Confluence) and strong executive/customer-facing leadership during high-stakes customer commitments.”
Shiva Arcot
Director of Security & Data Platform Engineering specializing in AI-driven cloud security
“Player-coach engineering leader focused on scalable data security scanning and risk detection in hybrid cloud, owning architecture and core implementation of an incremental/parallel DSPM scanning engine. Shipped production improvements including 60% lower scan latency and 30% fewer false positives, with strong emphasis on correctness under concurrency, multi-tenant observability (SLOs/burn-rate alerts), and disciplined rollout practices (feature flags, shadow scans, canaries).”
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.”
Sujit Singh
Engineering Director specializing in backend & data platforms for enterprise SaaS and cybersecurity
“Backend/data engineering player-coach on a UEBA cloud security analytics platform who standardized MLOps and detection development for 180+ detections, cutting ship time from 6–7 weeks to ~3 weeks while reducing false positives. Proven at operating large-scale streaming + Spark systems (200K+ events/sec, 100+ TB/day), driving major reliability/cost improvements, and leading incident response and team execution through GA.”
Chinedu Enwere
Mid Software Engineer specializing in distributed backend systems
“Engineering candidate deeply embedded in AI-native development, currently using tools like Cursor and Claude Code to generate most of their code and building internal agents for on-call monitoring, anomaly detection, and automated incident mitigation. Particularly interesting for teams exploring AI-first engineering workflows, multi-agent development setups, and operational automation at scale.”
Martin Bally
Executive CISO/CTO specializing in global cybersecurity, risk management, and compliance
“Founder building a digital transformation startup that combines execution, GCC/center-of-excellence setup to fund transformation via labor arbitrage savings, and an AI/data analytics platform with live dashboards. Previously delivered multiple Fortune 500 transformations and created fraud-detection value by reusing in-vehicle cybersecurity data to identify warranty and odometer rollback fraud; currently raising capital through executive/board networks and Silicon Valley investors.”
Jun Ouyang
Principal Software Engineer / Tech Lead specializing in distributed systems, payments, and reliability
“Backend engineer with DoorDash experience building production-critical systems spanning LLM-based real-time safety moderation (SendBird callbacks + ChatGPT risk scoring with automated actions) and large-scale payments data pipelines (Kafka to CockroachDB with aggregation APIs). Also led cross-team reliability work to standardize SLOs and drove an incident redesign from batch pull to real-time push callbacks to eliminate critical-event latency.”