AI Architect

Role Overview

• Own the end-to-end technical architecture for AI-powered solutions across multiple customer engagements
• Evaluate, select, and standardize 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
• 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
• 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
• 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

Benefits

• social events
• chill-out spaces
• remote working
• flexi-hours
• professional development courses