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Designs and implements large-scale cloud and hybrid infrastructure solutions while supporting customer development, testing, and production environments. Leads a small Cloud Engineering team, administers Azure and Kubernetes platforms, networking, Linux systems, GitLab CI/CD pipelines, security tooling, and infrastructure-as-code. Builds testing infrastructure and container or machine images, performs vulnerability scanning, and manages upgrades and maintenance. Communicates technical designs to executives and nontechnical stakeholders.

Develop and maintain C/C++ applications on Linux, using command-line toolchains and CI/CD pipelines to compile, test, containerize, and deliver software artifacts. Apply Git, code reviews, static analysis, troubleshooting, design patterns, architectural principles, and SOLID practices to build scalable systems. Collaborate with development, operations, security, and business teams to ensure reliable, secure software delivery.

Administer and secure Microsoft Azure environments supporting development, testing, and production deployments. Manage networking, Linux systems, GitLab CI/CD pipelines, vulnerability scanning, security tooling, Kubernetes clusters, infrastructure images, and ongoing platform upgrades. Use Infrastructure as Code and Bash or Python automation while providing technical leadership and consulting support for customer systems.

Lead the design, development, and operation of secure, scalable distributed APIs and data services in cloud environments. Responsibilities include architecting GraphQL and REST services, developing TypeScript and Go applications, managing serverless and containerized workloads, implementing infrastructure as code and CI/CD, designing data and messaging workflows, establishing observability and resiliency patterns, and ensuring security and compliance. The role also mentors engineers and collaborates with product, compliance, and security teams.

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Build and scale production enterprise AI applications using RAG, LLM integrations, agents, vector search, and prompt evaluation. Develop Python/FastAPI backends, React/Next.js frontends, real-time services, and database architectures. Deploy AI workloads with Docker, Kubernetes, CI/CD, cloud infrastructure, and Terraform. Implement monitoring, testing, security, observability, and cost optimization for reliable, scalable AI systems.