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 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.

The Opportunity

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

Responsibilities

Core – Deep Cloud Expertise

Be the trusted cloud engineer customers lean on for high‑impact technical optimization work across cost, reliability, security, and performance.

Design and help implement solutions that:

• Improve cost efficiency (rightsizing, reservations/commitments, storage optimization, etc.).
• Increase reliability and resilience (HA/DR architectures, SLO/SLA‑aware designs).
• Strengthen security posture (IAM, network segmentation, data protection, least‑privilege).
• Reduce operational toil (automation, self‑service, guardrails, policy enforcement).
• Plan and deliver structured engagements such as Cloud Optimization Sessions, cost/efficiency/performance workshops, security posture or reliability reviews, and architecture deep dives / "well‑architected" style assessments.
• Respond to Expert Inquiry / support requests that require deep cloud engineering expertise, ensuring high‑quality, well‑explained resolutions.
• Bring domain depth in ML / GenAI: deploying and operating ML/GenAI workloads (training and inference), GPU utilization, scaling, and cost control; MLOPS and integrating workloads with monitoring, logging, and FinOps; safe and efficient use of managed AI services.

Builder – Product Feedback & Contribution

Turn one‑off field work into reusable assets that improve both customer outcomes and the product itself.

• Convert one‑off customer solutions into Gravel Roads - reusable patterns such as playbooks, Terraform modules, CloudFlow templates, cloud diagrams, Composer Recipes, DCI Insights, and internal/external documentation.
• Provide structured feedback to the DoiT Product and Engineering teams on product gaps, new opportunities for automation, telemetry, and tracking.
• Contribute directly to DCI where appropriate—from feature requests and feedback to contributing code and owning specific DCI features end‑to‑end.
• Build agent skills, scripts, and internal tooling that codify your expertise and scale it across the team.
• Contribute to internal enablement: share learnings via documentation, demos, office hours, or training sessions.

Account Team – Embedded Execution

Operate as an embedded technical partner inside the account team.

• Work in the account team model alongside Customer Success Managers (CSMs) and Account Managers (AMs) to deliver impactful outcomes.
• Own the technical depth lane: technical deployment & integration, automation & platform adoption, signal‑based proactive engagement, and repeatable Cloud Optimization solutions.
• Partner with customers' engineers, architects, and FinOps teams to translate pain points into concrete technical optimization plans.
• Co‑deliver complex or multi‑domain engagements with peer FDEs (e.g., infra + data + ML/GenAI).
• Communicate complex technical topics clearly to both engineers and non‑technical stakeholders (FinOps, finance, leadership).
• Contribute to a culture of continuous improvement within the global FDE community.

Product Expert – DoiT Cloud Intelligence™ (DCI)

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 dashboards, identify cost/risk/reliability opportunities, build queries and recipes, create CloudFlow automations, and optimize data.
• Help customers embed DCI into existing observability, CI/CD, and governance processes.

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.
• Experience with Amazon Q Business and Amazon Q Developer (or similar tools).

ML Pipelines, Data & MLOps

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

DevOps, MLOps, Governance & Security

• Hands-on experience with CI/CD for AI/ML (AWS CodePipeline, CodeBuild, SageMaker Pipelines).
• Proficiency monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor.
• Strong understanding of AI governance, security, and compliance on AWS (IAM, KMS, data privacy patterns, bias detection).

Multi-Cloud Awareness & Collaboration

• Working knowledge of Google Cloud AI tools (Vertex AI, Cloud AutoML, BigQuery ML) for multi-cloud context.
• Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product.
• Excellent communication skills across technical and business audiences; strong ownership mentality.

Bonus Points

• BA/BS degree in Computer Science, Mathematics, or a related technical field.
• Additional data or AI certifications (e.g., AWS/GCP data certifications, Stanford, Coursera, Udacity, MIT, eCornell).
• Experience with modern RLHF, advanced fine-tuning, hybrid AI architectures, or Hugging Face.
• Prior experience as an ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment.

Benefits

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