.png){kind=logo}
Vetted Performance Tuning Professionals
Pre-screened and vetted.
Senior Python Full-Stack Engineer specializing in cloud-native FinTech and insurance platforms
Mid-level Full-Stack Software Engineer specializing in Python and React
Mid-Level Full-Stack Software Engineer specializing in FinTech and Healthcare IT
Mid-level Backend Engineer specializing in Node.js/TypeScript APIs and developer platforms
Mid-Level Software Development Engineer specializing in full-stack and cloud microservices
Senior DevOps/SRE Engineer specializing in multi-cloud Kubernetes and DevSecOps
Senior AIX/Linux Engineer specializing in IBM Power Systems, PowerVM, and High Availability
Senior Lead Data Engineer specializing in cloud data platforms and real-time ML pipelines
Mid-level Cloud & DevSecOps Engineer specializing in AWS, Java microservices, and SOC operations
Executive CTO and SaaS Platform Architect specializing in scalable data systems
Mid-level Backend Python Developer specializing in APIs, ETL, and transaction processing
Senior Full-Stack Engineer specializing in AI-native SaaS and cloud infrastructure
Mid-level Full-Stack Developer specializing in Python and Next.js cloud-native systems
Senior .NET Developer specializing in cloud-native microservices and modern web apps
thomas caine
Senior DevOps & Linux Systems Engineer specializing in AWS cloud infrastructure
“UNIX operations-focused candidate with hands-on AIX/Solaris troubleshooting and Bash automation experience, including resolving a production connectivity outage caused by a host table IP misconfiguration. Has supported a re-platform migration of a home-grown application by working directly with developers and running parallel testing prior to cutover; no direct IBM Power (VIOS/HMC/LPAR) or NIM/PowerHA experience.”
Senior Software Engineer specializing in backend systems, cloud infrastructure, and applied AI/ML
Mid-level Data Engineer specializing in cloud data pipelines and streaming analytics
Vijay Kumar
Mid-level Cloud/DevOps Engineer specializing in AWS automation and CI/CD
“AWS Cloud DevOps Engineer focused on production Linux environments, building secure CI/CD pipelines (Jenkins/GitHub) to deploy Dockerized services to AWS ECS and automating infrastructure with Terraform/CloudFormation. Strong in operational troubleshooting and scaling (CloudWatch-driven performance remediation, Auto Scaling/ELB, multi-AZ HA patterns), but explicitly does not have IBM Power/AIX or PowerHA/HACMP experience.”
SAI PRASHANTH
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
“Software engineer/product-minded builder who owns customer-facing products end-to-end and ships in 1–2 week increments using CI/CD, automated testing, and feature flags. Built a TypeScript/React/Node platform that cut page load times by 40% and scaled to 3x concurrent users, and designed RabbitMQ-based microservices with Prometheus/Grafana monitoring. Also delivered an internal real-time support analytics dashboard that reduced response times by 30%.”
Chandini Sattineni
Mid-level Software Engineer specializing in full-stack web, Go microservices, and AI integrations
“Backend/LLM engineer who ships production internal tooling end-to-end: automated data-request processing with monitoring-driven improvements (better error diagnostics and lower latency via query/index tuning). Also built a RAG-based internal Q&A system over company docs and operational logs with guardrails (similarity thresholds, fallbacks, response limits) and an eval loop using real user queries and human review to drive prompt/retrieval changes.”
bharath burgoju
Mid-Level Data Engineer specializing in cloud data pipelines and big data platforms
“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”