**Location**
Our **Senior Cloud Architect, ML/AI** will be an integral part of our general 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.
**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, 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.
For **Field Engineering**, you will focus more on **pre-sales, POVs, CloudBuild engagements, and partner-led growth motions**.
For **Delivery**, you will focus more on **install base health, product adoption, proactive engagements, and account-team work**.
**Responsibilities**:
**1.
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.
For **Field Engineering**:
- Partner with Account Executives, Solution Engineers, and Growth FDEs to **shape and win opportunities** across all four GTM pillars in-region (product adoption, new logo acquisition, install base expansion, partner-led growth).




- Serve as **technical lead for extended POVs and CloudBuild engagements** focused on AI/ML and GenAI, demonstrating clear value and de-risking customer adoption.
- Build compelling **technical narratives and demos** that support revenue-generating motions, including co-sell initiatives with CSP partners.
For **Delivery**:
- Act as a **named technical advisor** for a portfolio of existing customers, working within account teams (Account Manager, CSM, FDE) to improve install base health and outcomes for AI/ML and GenAI workloads.
- Lead proactive **"Get FDE"-style engagements** where AI/ML expertise is needed to unblock customers, reduce risk, or improve the impact of DoiT Cloud Intelligence.
- Participate in structured **account-team routines** (e.g., objective setting, quarterly environment reviews) to keep AI/ML architectures aligned with customer goals and product adoption opportunities.
**2.
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).
For **Field Engineering**:
- Design and run **service-led product adoption plays** that use AI/ML and GenAI projects to drive deeper adoption of DoiT's platforms, in partnership with Growth leadership.
- Ensure AI/ML-focused CloudBuild and POV engagements include **mandatory product adoption playbooks**, with clear activation and follow-up criteria.
For **Delivery**:
- Execute against delivery programs and automations that **detect product struggles** (e.g., customer

📌 Senior Cloud Architect, Ml/Ai (Xico)
🏢 Doit
📍 Xico