Senior Cloud Architect

Job Description

Become an expert in DCI and use it hands‑on to drive concrete customer outcomes.

• Master DoiT Cloud Intelligence™ products and services — including Cloud Analytics, DCI Insights, Cloud Composer, CloudFlow, DataHub, PerfectScale, and other Enterprise Platforms.
• Use DCI hands‑on to:
  • Build and operationalize Cloud Analytics and Allocations to create dashboards and reports for customer engineering, finance, and leadership.
  • Use DCI Insights to identify and prioritize cost, risk, and reliability opportunities, and shepherd them through to closure.
  • Implement Cloud Composer queries, build recipes that result in hand-crafted insights across all customers’ engineering use cases.
  • Build CloudFlow automations (e.g., anomaly routing, scheduled actions, guardrails, policy enforcement).
  • Use Built in Integrations such and utilize DataHub and other workload‑intelligence features to optimize key business and workload data inside DCI.
• Help customers embed DCI into existing observability, CI/CD, and governance processes so it becomes trusted and indispensable in day‑to‑day cloud operations.
  

Qualifications

Experience

• 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads.
• Proven track record designing and operating large, distributed systems on AWS, selecting appropriate services and patterns to meet business and technical goals.
  

AWS & GenAI / ML Expertise

• Advanced proficiency with AWS services relevant to AI/ML and GenAI.
• Hands-on experience with Amazon Bedrock for deploying and scaling foundation models and Generative AI workloads.
• Experience fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart).
• Strong prompt engineering skills and familiarity with rigorous model evaluation (quality, safety, performance).
• Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks and integrate with existing systems.
• Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to accelerate insight generation and development workflows.
  

ML Pipelines, Data & MLOps

• In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability.
• Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker for model development, fine-tuning, and deployment.
• Experience with distributed training (multi-GPU or multi-node) and performance optimization for inference.
• Strong data-engineering skills on AWS: Amazon S3, AWS Glue, Lake Formation, Redshift for AI/ML data pipelines.
• Experience building end-to-end AI/ML workflows using services like AWS Lambda, Step Functions, API Gateway, and containerized deployments on Amazon EKS / AWS Fargate.

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