AI Integration Architect

As an AI Integration Architect, your role involves designing resilient, enterprise-grade ecosystems across AI, data, and cloud boundaries. You will be responsible for transforming ambiguous business requests into concrete technical blueprints, building foundational platforms, and ensuring production stability during challenges. Your expertise will be crucial in handling complex integration challenges within the team. In your role, you will have the following key responsibilities: - Convert vague business goals into clear technical roadmaps. - Align senior stakeholders and provide engineering teams with actionable, structured designs. - Define API contracts, data flows, rate-limiting, and failure-handling strategies upfront. - Build and maintain secure, solid developer-facing API platforms, handling gateway configurations, quota management, and complex authentication. - Connect AI components into production systems using Python/FastAPI, backed by strict contracts and operational runbooks. - Design AWS-based asynchronous pipelines with built-in fault tolerance, including dead-letter queues (DLQs), smart retries, and distributed tracing. - Lead incident triage, identify root causes, and establish high-quality reusable patterns and API standards. - Review infrastructure and frontend code to guide delivery, ensuring alignment across the stack without needing to write frontend code yourself. In terms of qualifications and experience, you should have: - 10+ years of engineering experience with proven ownership in technical leadership or architecture roles. - A track record of thriving in ambiguity and maintaining high execution rigor. - Experience in documenting clear API contracts, architecture decision records (ADRs), and runbooks. Your core technical stack should include expertise in: - Backend: Expert-level Python and FastAPI. - Cloud (AWS): Deep expertise in Serverless, Containers, Security/Networking, and Cost Optimization. - DevOps & Infra: Hands-on experience with Terraform, Docker, and CI/CD pipelines. - Security & Observability: Implementation of Zero-Trust, SSO/OAuth, structured logging, and distributed tracing. Additionally, your proficiency in AI & Data Ecosystem should cover: - AI Architecture: Understanding of RAG, embedding pipelines, multi-agent workflows, and managing inference latency. - Vector Databases: Practical knowledge of scaling tools like Pinecone, FAISS, Weaviate, or Qdrant. - Responsible AI: Ability to architect guardrails into data pipelines, including monitoring for bias, explainability, and safety. (Note: Data Science/Model training expertise is not required for this role.) This job opportunity is from iimjobs.com. As an AI Integration Architect, your role involves designing resilient, enterprise-grade ecosystems across AI, data, and cloud boundaries. You will be responsible for transforming ambiguous business requests into concrete technical blueprints, building foundational platforms, and ensuring production stability during challenges. Your expertise will be crucial in handling complex integration challenges within the team. In your role, you will have the following key responsibilities: - Convert vague business goals into clear technical roadmaps. - Align senior stakeholders and provide engineering teams with actionable, structured designs. - Define API contracts, data flows, rate-limiting, and failure-handling strategies upfront. - Build and maintain secure, solid developer-facing API platforms, handling gateway configurations, quota management, and complex authentication. - Connect AI components into production systems using Python/FastAPI, backed by strict contracts and operational runbooks. - Design AWS-based asynchronous pipelines with built-in fault tolerance, including dead-letter queues (DLQs), smart retries, and distributed tracing. - Lead incident triage, identify root causes, and establish high-quality reusable patterns and API standards. - Review infrastructure and frontend code to guide delivery, ensuring alignment across the stack without needing to write frontend code yourself. In terms of qualifications and experience, you should have: - 10+ years of engineering experience with proven ownership in technical leadership or architecture roles. - A track record of thriving in ambiguity and maintaining high execution rigor. - Experience in documenting clear API contracts, architecture decision records (ADRs), and runbooks. Your core technical stack should include expertise in: - Backend: Expert-level Python and FastAPI. - Cloud (AWS): Deep expertise in Serverless, Containers, Security/Networking, and Cost Optimization. - DevOps & Infra: Hands-on experience with Terraform, Docker, and CI/CD pipelines. - Security & Observability: Implementation of Zero-Trust, SSO/OAuth, structured logging, and distributed tracing. Additionally, your proficiency in AI & Data Ecosystem should cover: - AI Architecture: Understanding of RAG, embedding pipelines, multi-agen