AI Architect

Role Overview

• Own the end-to-end technical architecture for AI-powered solutions across multiple customer engagements, evaluating, selecting, and standardizing AI technologies, frameworks, and platforms for the organization.
• Create reference architectures, design patterns, and best practices for AI solution delivery; lead architectural reviews and provide technical governance across delivery teams; and collaborate with product owners, data scientists, and engineering leads to translate business requirements into scalable technical designs.
• Design data architectures that support AI workloads—ingestion, processing, storage, and retrieval—establish and champion engineering standards for AI quality, testing, security, and observability, and guide build-vs-buy decisions while managing vendor/partner technology relationships.
• Mentor and uplift engineering teams in AI architecture and best practices, stay current with the rapidly evolving AI landscape to bring actionable insights to the organization, and present architectural visions and technical strategies to senior stakeholders and customers.

Requirements

• Expertise in agentic AI and multi-agent orchestration (tools, planning, reflection, chain-of-thought)
• Build AI evaluation frameworks with metrics, benchmarks, and QA for non-deterministic systems
• Proficiency in prompt engineering, management, and security (injection prevention, guardrails)
• Ensure reliable model serving, GPU optimization, and cost-aware inference
• Design end-to-end solutions across frontend, backend, data, and AI layers
• Create architectural decision records, reference architectures, and technical design documents
• Master distributed systems, event-driven architectures, and microservices
• Develop AI data architecture (modeling, pipelines, governance)
• Design API patterns (REST, GraphQL, gRPC) for robust AI service integration
• Address security with threat modeling, responsible AI, data privacy, and compliance
• Maintain expert-level Azure/AWS/GCP cloud architectures
• Leverage AI managed services (Bedrock, SageMaker, Vertex AI, Azure OpenAI)
• Use serverless and container patterns (ECS/EKS, Cloud Run, Lambda)
• Build vector/search infrastructure (Pinecone, Weaviate, pgvector, OpenSearch)
• Apply Infrastructure as Code (Terraform, CDK, Pulumi) and GitOps
• Focus on AI FinOps and cost optimization
• Excel in Python for prototyping, review, and critical decisions
• Use TypeScript/JavaScript for full-stack design
• Employ AI frameworks (Semantic Kernel, AgentCore, etc)
• Ensure AI observability—tracing, logging, monitoring, and drift detection
• Lead technical teams and mentor engineers
• Communicate complex concepts to non-technical stakeholders
• Run architecture reviews, design workshops, and governance

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Google Cloud Platform
• GraphQL
• GRPC
• JavaScript
• Microservices
• Python
• Terraform
• TypeScript