Software Development Cloud Architect

WE’RE HIRING!

At HTG, you’ll push boundaries with the latest tech (in some cases inventing it) and collaborate with a team that loves what they do. Join a design services company at the forefront of global technology and innovation where the products you help build are used around the world.

Your next chapter starts here.

You will lead with expertise in:

• Design and implement AI-driven developer workflows that optimize coding, testing, deployment, and debugging.

• Build intelligent agents to recommend best practices, detect security risks, and ensure compliance with standards such as SOC, FedRAMP, and AWS GovCloud.

• Integrate advanced language models (e.g., GPT, Claude, CodeLlama) into developer tools and CI/CD pipelines for:

o Automated code generation, bug detection, and test creation.

o Pull request review automation and merge risk prediction.

o Context-aware debugging assistance during development.

• Define integration strategies and APIs that enable shared AI components and models across teams or business units.

• Fine-tune large language models to align with internal codebases, architectures, and operational requirements.

• Design scalable CI/CD pipelines supporting multi-cloud and on-premise environments using platforms such as ArgoCD, Jenkins, or Tekton.

• Embed AI into deployment workflows for predictive analysis, rollback automation, and zero-downtime release strategies.

• Enhance observability with AI-driven log summarization, anomaly detection, event correlation, and proactive alerting.

• Develop self-healing infrastructure that reduces operational overhead and accelerates developer productivity.

• Automate vulnerability detection, prioritization, and remediation using AI-based scanners and classifiers.

• Use AI and NLP to improve infrastructure-as-code quality and generate automated fixes for configuration issues.

• Convert compliance documentation into policy-as-code frameworks to streamline governance and auditing.

• Collaborate with security and infrastructure teams to strengthen platform resilience and reduce incident response times.

• Partner across engineering, QA, SRE, and documentation teams to align AI initiatives with broader technical goals.

• Lead proof-of-concept efforts and performance benchmarks to validate and scale AI-driven workflows.

• Promote AI/ML adoption through mentorship, training, and architectural guidance.

• Coordinate with other teams or units to share, evaluate, or co-develop AI tools, APIs, and model capabilities.

• Leverage AI to automate test case generation for APIs, microservices, and infrastructure.

• Use AI tools to generate test data, assess coverage, and recommend testing improvements.

• Implement AI-based load testing pattern generation and automated analysis of test results.

Requirements

• Must be fluent in English

• 7+ years of experience in cloud architecture, full-stack development, or platform engineering

• 2+ years of experience applying AI or machine learning within software engineering workflows (for example, intelligent code assistants, generative coding tools, or custom LLM-based integrations)

• Cloud-native platforms such as AWS, Google Cloud, or Microsoft Azure

• AI agent or workflow development using modern orchestration frameworks (e.g., LangGraph or similar tools)

• Continuous integration and delivery systems such as ArgoCD, Tekton, or Jenkin

• Infrastructure as code using Terraform, Crossplane, or equivalent technologies

• Full-stack development with frameworks like React or Vue.js and backend stacks such as Node.js, Python, or Java

• Containerized and serverless architectures including Kubernetes, EKS, GKE, AWS Lambda, or Cloud Run

• Monitoring and observability tools such as Prometheus, OpenTelemetry, Datadog, or ELK

• Demonstrated success in designing, building, or integrating AI-enhanced developer or platform tooling that improves efficiency, reliability, or developer experience

Desired skills:

• Experience collaborating across teams or departments to design, adopt, or scale shared AI models, services, or infrastructure components

• Exposure to MLOps platforms such as MLflow, Kubeflow, or SageMaker Pipelines