Senior Cloud Architect, Field Engineering (GenAI Focus)

Job summary

DoiT is seeking a Senior Cloud Architect with a focus on GenAI to join their Field Engineering team. This role involves leading hands-on delivery for GenAI implementation engagements, driving outcomes across product adoption, new logo acquisition, install base expansion, and partner leadership, and transforming field work into repeatable solutions. The position requires expertise in AWS, modern AI/GenAI architectures, and customer-facing technical consulting.

Benefits

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 transition between technical depth and customer-facing communication.
• Experience leading workshops, discovery sessions, implementation activities, or technical proof-of-value engagements.
• Strong judgment in ambiguous environments; able to simplify, prioritize, and advance work without heavy process overhead.
• Comfortable collaborating with sales, delivery, customer success, product, and partner stakeholders.
• Demonstrated ownership mentality: escalate early, resolve fast, and take responsibility for outcomes.
• Experience delivering GenAI workshops, technical assessments, or customer implementation engagements.
• Experience with AWS Migration Acceleration Program (MAP), partner-funded implementation programs, or similar structured cloud adoption programs.
• Experience building reusable technical assets, templates, or playbooks to improve 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.

Responsibilities

• Lead hands-on delivery for GenAI implementation engagements, funded implementation projects, and technical proof-of-value engagements.
• Translate customer goals into practical architectures, implementation plans, and measurable technical outcomes.
• Build, configure, and validate AWS-native AI and data solutions, emphasizing production-ready architectures and services.
• Own technical execution from discovery through delivery, including design reviews, workshops, implementation support, and executive-ready readouts.
• Address complex customer situations requiring technical depth, speed, and credibility.
• 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 through technical consulting, implementation engagements, and proof-of-value work.
• 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 positioning DoiT as a strategic technical partner in AI-related motions.
• Identify patterns, reusable assets, and solutions that can become standard delivery approaches, playbooks, or product feedback.
• Transform successful one-off customer work into repeatable solution packages, templates, and standardized offerings.
• Contribute to standardization of engagement sizing, delivery approach, and technical assets to improve team efficiency.
• Partner cross-functionally with Solution Engineers, Account Managers, Customer Success Managers, Engagement Managers, and partner teams to scope and execute work.
• Provide technical leadership during discovery, planning, handoff, and delivery.
• Ensure customer engagements are well-scoped, well-documented, and tied to clear success criteria.
• Maintain clear visibility into active work, risks, dependencies, and next steps.
• Utilize team operating systems and workflows to keep customer engagement data current and measurable.
• Contribute to adoption playbooks, funding workflows, Jira hygiene, and management cadence to scale the Field Engineering model.

Skills

Certifications

Relocation