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Vetted Azure Kubernetes Service Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in cloud lakehouse and streaming pipelines
Varaprasad Bathula
Intern AI/ML Engineer specializing in LLM applications and data infrastructure
“Hands-on LLM practitioner who built a production document-processing pipeline in Python, tackling long-document handling and latency with chunking/batching and a user-driven correction feedback loop. Experienced operationalizing AI workflows with Kubernetes (CronJobs, autoscaling, scheduled data cleaning and weekly retraining) and applying structured testing/evaluation (E2E, LLM-as-judge, HITL) while communicating solutions clearly to non-technical clients using visual diagrams.”
Omolara Okubanjo
Senior DevOps/DevSecOps Engineer specializing in AWS & Azure cloud infrastructure
“Infrastructure/DevOps-focused engineer working across Linux-based enterprise platforms that include IBM Power/AIX in a broader OpenShift/Kubernetes and cloud ecosystem. Built Azure DevOps CI/CD for containerized deployments and resolved a production deployment failure by tracing ImagePullBackOff to outdated registry credentials in Kubernetes secrets. Uses Terraform (with modular structure) plus Ansible to provision and standardize production environments with pipeline-based validation.”
“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”
Pratik Jaiswal
Mid-level AI/ML Engineer specializing in financial services ML and MLOps
“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”
Mid-level Software Engineer specializing in cloud-native microservices and real-time data pipelines
Intern Software Engineer specializing in cloud data platforms and full-stack systems
Senior DevSecOps Engineer specializing in secure cloud CI/CD and compliance
Senior Full-Stack Java Engineer specializing in cloud-native microservices and FinTech integrations
Mid-level Full-Stack Developer specializing in Python and React for e-commerce
Senior Full-Stack Engineer specializing in Python, cloud-native systems, and LLM applications
Mid-level DevOps & MLOps Engineer specializing in cloud-native CI/CD and Kubernetes
Mid-level DevOps Engineer specializing in multi-cloud Kubernetes and CI/CD automation
Senior DevOps Engineer specializing in cloud-native CI/CD and Kubernetes
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Senior Full-Stack Python Engineer specializing in cloud microservices and MLOps
Senior Full-Stack Java Developer specializing in microservices, cloud, and real-time systems
Senior Software Engineer specializing in cloud-native backend, ETL, and AI/ML on AWS
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG for financial services
Mid-level Full-Stack Software Engineer specializing in FinTech and cloud-native systems
Senior Full-Stack Software Engineer specializing in cloud microservices and GenAI
Mid-level Java Developer specializing in Spring Boot microservices (FinTech & Healthcare)