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Vetted Terraform Professionals
Pre-screened and vetted.
Mid-level Software Engineer specializing in backend microservices and cloud-native systems
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Mid-level Data Engineer specializing in AI/ML data platforms and real-time streaming
Mid-level Data Engineer specializing in cloud lakehouse and streaming pipelines
Mid-level Data Engineer specializing in streaming and cloud lakehouse platforms
Senior DevOps Engineer / AWS Solutions Architect specializing in Kubernetes and DevSecOps
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML systems
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Senior Software Engineer specializing in cloud platforms and distributed systems
Executive CTO/VP Engineering specializing in high-performance AI, data systems, and distributed infrastructure
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Executive Engineering Leader specializing in Telehealth Platforms and Healthcare IT
Matthew Mullins
Senior Software Engineer specializing in AI/ML backend and cloud infrastructure
“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”
Swastik Chowdhury
Mid-level Data Engineer specializing in cloud data platforms and real-time streaming
“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”
Ajith P
Mid-level Backend Software Engineer specializing in AI workflow automation for finance and healthcare
“Backend/AI engineer with healthcare domain experience who built a patient journey analytics API (FastAPI/PostgreSQL/Snowflake/Redis) and debugged peak-hour latency down from ~900ms to ~50ms via indexing and query optimization. Shipped an LLM-powered clinical summary/recommendation assistant end-to-end and designed a multi-step risk evaluation agent workflow with safety guardrails against hallucinations and unsafe outputs.”
Daniel Jeong
Junior Full-Stack Engineer specializing in real-time platforms and AI tools
“Early-career full-stack engineer with unusual depth in mission-critical environments: helped build a cybersecurity operations platform from scratch as the third engineer and shipped it to the National Election Commission of South Korea. Also worked on defense-focused situational awareness software, combining React/WebGL frontend performance work with backend data transformation for real-time weather and map overlays.”
Jugal Rayala
Mid-level Full-Stack Developer specializing in AI-powered cloud applications
“Full-stack engineer who has owned customer-facing AI recommendation and analytics dashboards end-to-end (backend APIs/data processing through React UI, deployment, and monitoring). Demonstrates strong systems thinking around scaling microservices—using observability, caching, async workflows, and resilience patterns—and also built an internal ops dashboard that became the default tool for on-call incident reviews.”
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.”