Senior Cloud Architect, Delivery (GenAI) - DoiT

About DoiT

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 multi-cloud problems and drive efficiency. With specializations in Kubernetes, GenAI, CloudOps, and more, we are an award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, working alongside more than 4,000 customers worldwide.

The Opportunity

As a Senior Cloud Architect (Gen-AI-Focused), you will be part of our global Forward Deployed Engineering (FDE) organization, working with rapidly growing companies worldwide. This role sits within FDE Delivery and focuses on install base health, product adoption, proactive engagements, and account-team work.

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 that influence the product roadmap.
• Focus on install base health, product adoption, proactive engagements, and account-team collaboration.

Responsibilities

Core: Deep Cloud Expertise

• Serve as the trusted Cloud SME customers lean on for high-impact technical optimization work across cost, reliability, security, and performance.
• Design and implement solutions that improve cost efficiency, increase reliability/resilience (HA/DR, SLO/SLA designs), strengthen security posture, and reduce operational toil.
• Plan and deliver structured engagements such as Cloud Optimization Sessions, workshops, security posture/reliability reviews, and Well-Architected style assessments.
• Respond to deep cloud engineering inquiries with high-quality resolutions.
• Bring domain depth in ML/GenAI: deploying and operating training and inference workloads, GPU utilization, scaling, cost control, MLOps, monitoring, logging, and FinOps.

Builder: Product Feedback & Contribution

• Convert customer solutions into reusable patterns ("Gravel Roads") such as playbooks, Terraform modules, CloudFlow templates, and documentation.
• Provide structured feedback to DoiT Product and Engineering teams on gaps, friction points, and telemetry needs.
• Contribute directly to DoiT Cloud Intelligence (DCI) through feature requests, code contributions, and feature ownership.
• Build agent skills, scripts, and internal tooling to codify and scale expertise.
• Support internal enablement via documentation, demos, office hours, and training sessions.

Account Team – Embedded Execution

• Operate as an embedded technical partner alongside Customer Success Managers (CSMs) and Account Managers (AMs).
• Partner with customer engineers, architects, and FinOps teams to translate pain points into concrete technical optimization plans.
• Co-deliver multi-domain engagements alongside peer FDEs.
• Communicate technical topics clearly to both technical and business stakeholders.

Product Expert: DoiT Cloud Intelligence™ (DCI)

• Master DCI products and services, including Cloud Analytics, DCI Insights, Cloud Composer, CloudFlow, and DataHub.
• Build and operationalize dashboards, reports, queries, and automated workflows.
• Help customers integrate DCI into existing observability, CI/CD, and governance processes.

Qualifications

• Experience: 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions in production, with a proven track record operating large, distributed systems on AWS.
• AWS & GenAI/ML Expertise: Advanced proficiency with AWS AI/ML services; hands-on experience with Amazon Bedrock, fine-tuning and deploying LLMs/multimodal models via Amazon SageMaker (including JumpStart); strong prompt engineering, model evaluation, and agentic workflows experience; familiarity with Amazon Q.
• ML Pipelines & Data: Deep knowledge of Amazon SageMaker (Pipelines, Model Monitor, Data Wrangler, Clarify); integration with PyTorch/TensorFlow; distributed training and inference optimization; data engineering on AWS (S3, Glue, Lake Formation, Redshift); workflows with Lambda, Step Functions, API Gateway, and Amazon EKS / AWS Fargate.
• DevOps & Governance: Hands-on CI/CD for AI/ML (CodePipeline, SageMaker Pipelines); monitoring with Amazon CloudWatch; robust understanding of AI governance, IAM, KMS, data privacy, and bias mitigation.
• Multi-Cloud & Collaboration: Working knowledge of Google Cloud AI tools (Vertex AI, BigQuery ML); proven ability to mentor peers and collaborate cross-functionally.
• Soft Skills: Excellent technical and business communication skills, strong ownership mentality, and proven success in a remote-first, global environment.

Bonus Points

• BA/BS degree in Computer Science, Mathematics, or a related technical field.
• Relevant AWS/GCP data or AI certifications.
• Experience with RLHF, modern fine-tuning techniques, and Hugging Face ecosystem integration.
• Prior background as an ML Engineer, Data Scientist, or AI Architect in consulting or SaaS environments.