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Vetted Alerting Professionals
Pre-screened and vetted.
Senior Backend Engineer specializing in Python microservices, Kubernetes, and streaming systems
Mid-level Software Development Engineer specializing in cloud-native microservices on AWS
Mid-level Full-Stack .NET Developer specializing in insurance and healthcare systems
Mid-level Site Reliability/DevOps Engineer specializing in multi-cloud, Kubernetes, and observability
Senior Software Engineer specializing in data pipelines and financial data platforms
Mid-level Full-Stack Product Engineer specializing in SaaS and AI workflows
Mid-level QA/Systems Analyst specializing in test tooling, observability, and healthcare data migration
Mid-Level Full-Stack Software Engineer specializing in cloud-native backend systems
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Senior DevOps & Cloud Engineer specializing in Kubernetes, IaC, and DevSecOps
Senior DevOps Engineer specializing in cloud-native Kubernetes and DevSecOps
Senior Production Application Support Engineer specializing in banking and financial systems
Mid-level Full-Stack Java Developer specializing in cloud microservices and React
Senior Full-Stack Software Engineer specializing in cloud-native microservices and event-driven systems
Mid-level Software Development Engineer specializing in backend systems and data pipelines
Mid-level Data Engineer specializing in big data pipelines and cloud data platforms
Senior DevOps & Cloud Infrastructure Architect specializing in multi-cloud platforms
Mid-level Software Engineer specializing in full-stack, cloud, and AI systems
Senior DevOps/Site Reliability Engineer specializing in multi-cloud Kubernetes platforms
Likitha Kukunarapu
Mid-level Applied AI Engineer specializing in data engineering and healthcare AI
“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”