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Vetted Azure Kubernetes Service Professionals
Pre-screened and vetted.
Mid-level DevOps Engineer specializing in cloud infrastructure and Kubernetes
Mid-level Full-Stack Developer specializing in Python and Next.js cloud-native systems
Mid-level Backend Software Engineer specializing in distributed systems and cloud-native microservices
Senior Azure DevOps / SRE Engineer specializing in cloud infrastructure automation
Mid-level Software Engineer specializing in FinTech and scalable backend systems
Sai Krishna mekala
Mid-level Full-Stack Software Engineer specializing in cloud microservices and AI search
“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”
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.”
Mid-level Cloud DevOps Engineer specializing in Kubernetes, CI/CD, and IaC
Mid-Level Software Development Engineer specializing in backend microservices and cloud-native IoT
Senior Backend Software Engineer specializing in Java microservices and cloud platforms
Mid-level Software Engineer specializing in AI and cloud-native data platforms
Senior Full-Stack Engineer specializing in cloud-native platforms, DevOps, and Kubernetes
Mid-level Backend Software Engineer specializing in cloud microservices and distributed systems
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices
Senior Cloud DevOps/SRE Engineer specializing in Azure and Kubernetes
Mid-level Machine Learning Engineer specializing in LLMs, Generative AI, and MLOps
Mid-level DevOps Engineer specializing in Azure cloud infrastructure and CI/CD
Senior Full-Stack Engineer specializing in Python, cloud-native microservices, and APIs
Priyanka Ramesh
Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React
“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”
Boris Tengweck
Senior Cloud Security & DevOps Engineer specializing in AWS and regulated compliance
“Cloud/Linux infrastructure engineer focused on high-availability AWS platforms (EKS/EC2) with strong observability and incident response ownership. Has hands-on experience building secure CI/CD (GitHub Actions/Jenkins) and Terraform-based IaC at scale, including resolving real production issues like peak-traffic latency and Terraform remote state locking; transparent about not having direct IBM Power/AIX/PowerHA production ownership.”
Chien-Ting Hung
Director-level AI Engineer specializing in computer vision and LLM/RAG platforms
“Hands-on LLM/RAG engineer with production experience improving retrieval quality and stability by addressing messy data, vector DB inaccuracy, and top-K issues—ultimately redesigning to hybrid search with tuned keyword/semantic weighting and MCP-based data supplementation. Also brings strong AKS/Kubernetes deployment experience, optimizing CI/CD speed via lightweight local Docker validation and decomposing pods to avoid full rebuilds, plus a metrics-driven approach to agent/workflow testing and traceability.”