Original listing text, shown exactly as published by the company.
The Opportunity
We are hiring a Senior Cloud Architect to strengthen our AWS-focused AI Field Engineering capability inside Customer Experience. This is a customer-facing, delivery-heavy role focused on GenAI advisory and implementation engagements, business value assessment engagements, AWS-funded adoption programs, and complex technical adoption projects that help customers move from exploration to production outcomes.
This role is designed as a more clearly defined Field Engineering job within the existing organization, not as a separate standalone team. The goal is to add focused AI delivery capacity, reduce single-person dependency risk, and scale repeatable customer outcomes through better execution, enablement, and feedback into the wider Field Engineering motion.
You will contribute directly to the four Field Engineering growth pillars: product adoption, partner leadership, new logo acquisition, and install base expansion.
To support your success, you'll have access to structured enablement programs, delivery playbooks, and reusable technical assets built by the team to accelerate your ramp and delivery effectiveness.
What You Will Do
1. Deliver high-impact AI engagements
- Lead hands-on delivery for GenAI implementation engagements, funded implementation projects, technical proof-of-value engagements, and other customer-facing AI initiatives.
- Translate customer goals into practical architectures, implementation plans, and measurable technical outcomes.
- Build, configure, and validate AWS-native AI and data solutions, with emphasis on production-ready architectures and services.
- Own technical execution from discovery through delivery, including design reviews, workshops, implementation support, and executive-ready readouts.
- Step into complex customer situations where technical depth, speed, and credibility are required.
2. Drive outcomes across the four FE growth pillars
- Support product adoption by helping customers implement and integrate DoiT products as part of AI engagements and broader cloud initiatives.
- Contribute to new logo acquisition by using technical consulting, implementation engagements, and proof-of-value work to help open and progress new opportunities.
- Expand the install base by helping existing customers adopt advanced features, launch new workloads, and move to higher-value product and service motions.
- Strengthen partner leadership by collaborating with AWS partner teams, supporting funded programs, and helping DoiT show up as a strategic technical partner in AI-related motions.
3. Turn field work into repeatable plays
- Identify patterns, reusable assets, and “gravel road” solutions that should become standard delivery approaches, playbooks, or product feedback.
- Help move successful one-off customer work into repeatable solution packages, templates, and standardized offerings for the broader team.
- Contribute to standardization of engagement sizing, delivery approach, and technical assets to improve team efficiency over time.
4. Work cross-functionally to close and deliver
- Partner closely with Solution Engineers, Account Managers, Customer Success Managers, Engagement Managers, and partner teams to scope and execute the right work at the right time.
- Provide technical leadership during discovery, planning, handoff, and delivery.
- Help ensure customer engagements are well-scoped, well-documented, and tied to clear success criteria.
5. Operate with discipline
- Maintain clear visibility into active work, risks, dependencies, and next steps.
- Use the team’s operating systems and workflows to keep customer engagement data current and measurable.
- Contribute to adoption playbooks, funding workflows, Jira hygiene, and the management cadence needed to scale the Field Engineering model.
What Good Looks Like
Success in this role means you are helping Field Engineering scale AI delivery while creating measurable impact across the business. Over time, this role should help DoiT:
- increase delivery capacity for AI work without creating fragile single-threaded dependencies,
- convert more technical engagement into durable product adoption and retention,
- improve technical influence on pipeline and closed-won outcomes,
- and create repeatable AI delivery patterns that scale across regions and partner motions.
Qualifications
- Experience in customer-facing cloud architecture, technical consulting, solutions delivery, or field engineering.
- Hands-on experience with AWS in real customer environments.
- Working knowledge of modern AI and GenAI architectures on AWS — particularly Amazon Bedrock (Knowledge Bases, model evaluation, guardrails), retrieval-augmented generation (RAG) patterns with vector databases, and agentic AI design patterns. Familiarity with AWS CDK or similar infrastructure-as-code for deploying AI workloads.
- Ability to move between technical depth and customer-facing communication with ease.
- Experience leading workshops, discovery sessions, implementation activities, or technical POVs.
- Strong judgment in ambiguous environments; able to simplify, prioritize, and move work forward without heavy process overhead.
- Comfortable working across sales, delivery, customer success, product, and partner stakeholders.
- Natural ownership mentality: escalate early, resolve fast, and own the outcome.
Bonus Points
- Experience delivering GenAI workshops, technical assessments, or customer implementation engagements.
- Experience with the AWS Migration Acceleration Program (MAP), partner-funded implementation programs, or similar structured cloud adoption programs.
- Experience building reusable technical assets, templates, or playbooks that improved delivery leverage.
- Experience with Amazon SageMaker for MLOps workflows, model monitoring, or custom model deployment.
- Familiarity with agentic AI frameworks (e.g., AgentCore, Strands, or similar orchestration tools).
- Hands-on experience with vector databases (Aurora pgvector, OpenSearch) in production RAG architectures.
- AWS cloud certifications.
- Experience with DoiT products, cloud cost optimization, Kubernetes, data engineering, or platform modernization.
Why This Role Matters
This role helps DoiT meet growing customer demand for AI-focused technical delivery while keeping the work embedded in Field Engineering, close to customers, partners, and revenue motions. It is intentionally designed to deepen delivery strength, improve field leverage, and support the broader Field Engineering strategy across product adoption, new logo growth, install base expansion, and partner leadership.
Are you a Do’er?
Be your truest self. Work on your terms. Make a difference.
We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally.
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