Senior Cloud Architect, ML/AI

Role Overview

• Lead the design and implementation of production-grade ML and Generative AI solutions on AWS
• Act as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale
• Translate complex business problems into cloud architectures that are secure, reliable, cost-efficient, and observable
• Help evolve how DoiT uses AI/ML internally and with customers by turning one-off solutions into reusable patterns
• Lead discovery, architecture, and implementation for advanced ML and Generative AI workloads on AWS
• Own the technical success of your engagements
• Provide opinionated guidance on GenAI architectures
• Partner with Account Executives, Solution Engineers, and Growth FDEs to shape and win opportunities
• Serve as technical lead for extended POVs and CloudBuild engagements
• Build compelling technical narratives and demos
• Act as a named technical advisor for a portfolio of existing customers
• Lead proactive “Get FDE”–style engagements where AI/ML expertise is needed
• Participate in structured account-team routines
• Recommend and implement AI/ML-related capabilities in DoiT Cloud Intelligence
• Document and measure the business and technical impact of your work
• Design and run service-led product adoption plays
• Execute against delivery programs and automations
• Maintain a high personal bar for delivery quality
• Capture repeatable AI/ML patterns, reference architectures, and runbooks
• Identify and validate “gravel road” solutions
• Work with Product, R&D, and growth leaders to submit and champion these patterns
• Contribute to the FDE Delivery vision
• Collaborate closely with Sales, Customer Success, Product, and Business Systems Engineering
• Communicate clearly with both technical and non-technical stakeholders
• Work with cloud partner teams to align AI/ML initiatives to program funding

Requirements

• 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions
• Proven track record designing and operating large, distributed systems on AWS
• 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
• Strong prompt engineering skills and familiarity with rigorous model evaluation
• Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks
• Experience with Amazon Q Business and Amazon Q Developer
• In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify
• Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker
• Experience with distributed training and performance optimization for inference
• Strong data-engineering skills on AWS
• Experience building end-to-end AI/ML workflows
• Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines
• Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor
• Strong understanding of AI governance, security, and compliance on AWS
• Familiarity with AI ethics and bias detection/mitigation
• Working knowledge of Google Cloud AI tools
• Proven ability to mentor peers and collaborate across Sales, CS, and Product
• Excellent communication skills across technical and business audiences
• Natural ownership mentality
• Demonstrated ability to work effectively in a remote-first, global environment

Tech Stack

• AWS
• Cloud
• Distributed Systems
• PyTorch
• Tensorflow

Benefits

• Unlimited Vacation
• Flexible Working Options
• Health Insurance
• Parental Leave
• Employee Stock Option Plan
• Home Office Allowance
• Professional Development Stipend
• Peer Recognition Program