AI Platform Architect
About The Position
EverOps partners with enterprise engineering organizations to solve their hardest infrastructure and delivery challenges from the inside. As enterprises accelerate adoption of AI and GenAI, they need trusted technical leaders who can assess readiness, design secure architectures, and guide teams from strategy to execution. EverOps is seeking an AI Platform Architect to lead short-term, high-impact AI assessments and proofs of concept with enterprise clients. This individual will operate at an Architect level, combining deep AWS, data, and AI platform expertise with a consultative mindset. This role is designed for someone who can own ambiguity, lead discovery, and design scalable AI architectures that can be validated quickly. You will act as the technical lead for AI-focused assessment engagements, working directly with client stakeholders to: Identify and prioritize AI / GenAI use cases Evaluate data readiness and compliance constraints Recommend appropriate foundation models and architectures Design a phased implementation roadmap Deliver a PoC demonstrating technical feasibility You are expected to think and operate like an embedded architect and trusted advisor, not just an implementer.
Requirements
- 8+ years in Cloud, Platform, SRE, or Infrastructure Engineering roles
- Proven experience operating at an Architect level
- Strong client-facing and consultative experience
- Deep hands-on experience with AWS, including multi-account architectures and governance
- Strong knowledge of infrastructure as code (Terraform preferred)
- Experience designing secure, scalable platforms in AWS Organizations environments
- Practical experience with AI/ML platforms, preferably AWS-native (Bedrock, SageMaker, Glue, Athena, OpenSearch)
- Experience with GenAI architectures (RAG, embeddings, vector stores, agent frameworks)
- Familiarity with model evaluation, prompt engineering, and inference optimization
- Understanding of AI cost drivers and scaling considerations
- Strong grounding in SRE principles, observability, reliability, and operational excellence
- Experience designing production-ready systems with monitoring, alerting, and security baked in
- Ability to lead workshops, whiteboard architectures, and influence senior stakeholders
- Comfortable translating complex technical concepts into business-level narratives
- Strong written documentation and presentation skills
Nice To Haves
- Experience delivering AI assessments or AI strategy engagements
- Background in regulated industries (Healthcare, Fintech, Enterprise SaaS)
- Experience with FinOps for AI / GenAI cost governance
- AWS Certified Solutions Architect – Professional
- Experience building internal platforms or AI enablement frameworks
Responsibilities
- Lead technical workshops to identify, refine, and prioritize high-impact AI and GenAI use cases aligned with business objectives.
- Translate business problems into system design requirements and AI workflows.
- Assess existing data platforms, pipelines, governance, and accessibility for AI workloads.
- Evaluate data quality, lineage, security, and suitability for training, RAG, and inference patterns.
- Design AI architectures that comply with enterprise security, privacy, and regulatory constraints (PII, PHI, internal policies).
- Evaluate and design integrations across APIs, event streams, and existing systems.
- Evaluate and recommend foundation models and AI services, including Amazon Bedrock, Amazon Nova, and open-source models.
- Analyze tradeoffs across cost, latency, accuracy, and scalability.
- Design GenAI patterns such as RAG, agent workflows, and inference pipelines.
- Produce high-level and detailed AWS reference architectures for prioritized AI use cases.
- Define phased implementation roadmaps that balance speed, risk, and long-term maintainability.
- Identify PoC scope that can be executed within a short engagement.
- Partner with stakeholders to develop ROI and TCO models for AI initiatives.
- Provide cost modeling for model usage, data pipelines, infrastructure, and operations.
- AI assessment findings and recommendations
- Target-state AI platform architecture diagrams
- Data readiness and compliance assessment summaries
- Model evaluation and selection rationale
- Phased implementation roadmap
- PoC design and technical validation
- Executive-ready presentations and documentation
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
