[Hiring] Senior Cloud Architect, Delivery (GenAI) @DoiT

Role Description

Our Senior Cloud Architect will be an integral part of our global Forward Deployment Engineering team. This role is based remotely in the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel and is available to Full-Time Employees. The job is also open to contractors in Eastern Europe or Portugal.

As a Senior Cloud Architect, you will be part of our global Forward Deployed Engineering organization, working with rapidly growing companies in EMEA and around the world. This role sits within FDE Delivery and focuses on our install base, product adoption and customer health.

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.
• Focus more on install base health, product adoption, proactive engagements, and account-team work.

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).
• 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.
• 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.
• 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.
• Strong understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy patterns.
• Familiarity with AI ethics and bias detection/mitigation (e.g., using SageMaker Clarify or similar tools).
• 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.
• Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product.
• Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions.
• Natural ownership mentality: you escalate early, resolve fast, and own the outcome.
• Demonstrated ability to work effectively in a remote-first, global environment.

Requirements

• BA/BS degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.
• Additional data or AI certifications (e.g., AWS/GCP data certifications, reputable AI/ML programs such as Stanford, Coursera, Udacity, MIT, eCornell).
• Experience with modern RLHF, advanced fine-tuning techniques, and hybrid AI architectures.
• Familiarity with Hugging Face or similar open-source ecosystems integrated with AWS.
• Prior experience as a ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment.
• Experience with JIRA or similar tools for tracking work across delivery and product-feedback cycles.
• Exposure to Agile practices and frameworks commonly used for SaaS and cloud delivery.

Benefits

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

Company Description

DoiT is a global technology company that works with cloud-driven organizations to leverage public cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state—from planning to production.

Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide.