[Hiring] Senior Cloud Architect, ML/AI @DoiT

Role Description

Our Senior Cloud Architect, ML/AI will be an integral part of our global Forward Deployment Engineering team. This role is based remotely in the US, Colombia, Mexico, Canada, the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel. The job is also open to contractors in Eastern Europe or Portugal.

As a Senior Cloud Architect, ML/AI, you will be part of our global Forward Deployed Engineering organization, working with rapidly growing companies in EMEA and around the world. Depending on business needs, this role may be aligned to either Field Engineering (pre-sales + GTM) or FDE Delivery (install base, product adoption, customer health), with a common technical bar and shared expectations.

You will:

• Lead the design and implementation of production-grade ML and Generative AI solutions on AWS (with awareness of multi-cloud environments).
• Act as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale, from initial discovery through deployment and optimization.
• 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 and “gravel roads” that influence the product roadmap.

Responsibilities

• Customer Outcomes & Technical Leadership
  • Lead discovery, architecture, and implementation for advanced ML and Generative AI workloads on AWS, including data, training, inference, and integration layers.
  • Own the technical success of your engagements: clearly define outcomes, make tradeoffs visible, and ensure designs are production-ready (security, reliability, performance, cost).
  • Provide opinionated guidance on GenAI architectures (e.g., Amazon Bedrock, SageMaker, Q) and how they integrate with customers’ existing systems and processes.
• Product Adoption & Install-Base Impact (AI/ML & GenAI)
  • Recommend and implement AI/ML-related capabilities in DoiT Cloud Intelligence (e.g., CloudFlow, Insights, DataHub) as part of your customer engagements.
  • Document and measure the business and technical impact of your work, tying AI/ML initiatives to clear customer outcomes (cost, performance, reliability, productivity).
• Delivery Excellence, Practice Building & “Gravel Roads”
  • Maintain a high personal bar for delivery quality: clear scopes, realistic plans, strong communication, and crisp technical documentation.
  • Capture repeatable AI/ML patterns, reference architectures, and runbooks that other engineers can apply across customers.
• Collaboration, Partners & Cross-Functional Alignment
  • Collaborate closely with Sales, Customer Success, Product, and Business Systems Engineering to ensure AI/ML work is visible, repeatable, and connected to company priorities.
  • Communicate clearly with both technical and non-technical stakeholders, setting expectations and making risks and tradeoffs explicit.
• Operational Excellence & Ways of Working
  • Use and maintain the systems, templates, and workflows that support planning, observability, and quality across Customer Experience (e.g., JIRA, documentation standards, dashboards).
  • Contribute to internal enablement: teach other Doers about new AI/ML capabilities, share patterns, and help raise the bar globally for ML/AI expertise.

Qualifications

• 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.
• 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).
• 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.
• Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar.
• Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor.
• Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient to reason about multi-cloud architectures and integration points.
• Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions.

Benefits

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