Senior AI Architect

We are seeking a highly skilled and experienced Senior AI Architect to define and drive the architecture, design, and technical strategy for AI-enabled product platforms. This role will play a critical part in shaping our AI vision - embedding intelligence across applications, platforms, and workflows - while ensuring scalability, security, and maintainability. The ideal candidate brings deep expertise in AI-driven application development and must have strong, hands‑on experience architecting and operating solutions on AWS, including cloud‑native and AI workloads. This individual will partner closely with product and engineering leaders to translate business problems into scalable, AI-powered solutions that deliver real customer value. Responsibilities Define and own the AI and cloud architecture for product ecosystems, ensuring scalability, performance, security, and responsible AI usage. Establish best practices for AI system design on AWS, including model hosting, inference patterns, data pipelines, and governance. Lead architectural reviews for AI-driven systems, cloud platforms, integrations, and modernization initiatives. Drive architectural decisions around build vs. buy for AI capabilities, including AWS-native AI services. Collaborate with Product Managers and UX teams to translate business needs into AI-enabled product capabilities and AWS-based technical blueprints. Design architectures that support AI-first user experiences, intelligent automation, personalization, and decision support. Ensure platforms support rapid experimentation, model iteration, and continuous delivery of AI features in AWS environments. Balance near-term delivery with a long-term AI and cloud platform strategy. Provide hands‑on architectural guidance for building AI-powered full-stack applications using Python, Java, React, and modern AI frameworks. Design and integrate LLM-based services, ML models, and intelligent agents into production systems. Architect API-first and event-driven systems exposing AI capabilities via REST and GraphQL. Architect scalable, cost‑optimized cloud-native AI inference and data processing workloads on AWS. Drive decisions on data architectures, including SQL, NoSQL, vector databases, caching, and messaging. Integrate MLOps and DevOps practices, including CI/CD for models, monitoring, observability, and Infrastructure as Code (Terraform / CDK). Qualifications 5+ years strong, hands-on experience architecting and delivering AI-powered applications on AWS (mandatory). 10+ years in software development. Advanced proficiency in Python for AI/ML and backend systems; solid experience with Java and React. Experience integrating AI capabilities through API-first architectures (REST and GraphQL). Deep understanding of cloud-native AI architectures on AWS, including Kubernetes (EKS) based deployments. Experience with data pipelines, feature engineering, vector search, and inference patterns at scale. Strong knowledge of DevOps and MLOps, CI/CD, containerization (Docker, Kubernetes), and infrastructure automation on AWS. Proven track record of defining and delivering enterprise-scale, AI-enabled architectures in a product-driven organization. Ability to bridge business problems with AI and AWS architecture, influencing product roadmaps and investment decisions. Strong understanding of agile development and iterative delivery for AI systems. Excellent communication skills to articulate AI and AWS architecture to both technical and non-technical stakeholders. Ability to mentor and influence teams without direct authority. Strong problem-solving mindset, focused on delivering measurable business value through AI. Technical Expertise Extensive hands-on experience architecting and operating solutions on AWS, including EC2, S3, RDS, Lambda, EKS, and AWS-native AI/ML services (e.g., Bedrock, SageMaker). Experience with model hosting, inference patterns, data pipelines, and governance. Ability to design AI-first user experiences, intelligent automation, personalization, and decision support. Design of API-first and event-driven systems exposing AI capabilities via REST and GraphQL. Architect scalable, cost-optimized cloud-native AI inference and data processing workloads on AWS. Drive decisions on data architectures: SQL, NoSQL, vector databases, caching, and messaging. Integration of MLOps and DevOps practices: CI/CD, monitoring, observability, and IaC (Terraform/CDK). #J-18808-Ljbffr Namely