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Vetted Observability Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in LLM systems and conversational AI
Senior Backend Software Engineer specializing in FinTech and distributed systems
Senior Systems Engineer specializing in distributed ML infrastructure
Mid-level Full-Stack Engineer specializing in cloud-native FinTech systems
Senior Product Leader specializing in AI platforms and enterprise SaaS
Senior DevSecOps & Observability Engineer specializing in cloud automation and STIG compliance
Mid-level Backend Software Engineer specializing in scalable systems and healthcare workflows
Senior Software Engineer specializing in distributed systems and agentic AI platforms
Mid-Level Software Engineer specializing in cloud-native distributed systems
Mid-Level Software Engineer specializing in backend microservices and cloud automation
Mid-level Full-Stack Developer specializing in cloud-native FinTech and Healthcare IT
Director-level Engineering Leader specializing in SaaS, cloud, and AI/ML
Junior AI Engineer specializing in enterprise LLM and FinTech systems
Mid-level Software Engineer specializing in distributed systems and data platforms
Staff Software Engineer specializing in AI, data platforms, and LLM systems
Senior Site Reliability Engineer specializing in multi-cloud, Kubernetes, and observability
Executive technology leader specializing in AI, cloud, and healthcare transformation
Mid-level Platform Engineer specializing in cloud infrastructure and DevOps
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”
Sahithi S
Mid-level AI/ML Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”