Senior AI Architect

Company OverviewOur client is a leading global financial technology organization operating across multiple regulated jurisdictions, serving a large international client base through innovative trading, payments, and digital financial services solutions. The organization is undertaking a significant AI-driven transformation and is building a next-generation enterprise AI platform designed to power advanced analytics, intelligent automation, machine learning, and generative AI capabilities across its global operations.Role OverviewWe are seeking a highly experienced Senior AI Architect to design, build, and scale the organization's enterprise AI and data platform from the ground up. This is a hands-on leadership role for an individual who combines deep expertise in AI architecture, data engineering, cloud infrastructure, and software engineering with the ability to build production-grade systems.This role is responsible for establishing the technical foundations that will support the company's AI initiatives, including data platforms, MLOps capabilities, model deployment frameworks, cloud architecture, governance standards, and AI-enabled products. The successful candidate will operate as the senior technical authority for AI architecture while remaining actively involved in coding, implementation, and technical decision-making.Key ResponsibilitiesAI Platform ArchitectureDesign and build the enterprise AI platform architecture supporting all AI products and services across the organization.Develop scalable AI service frameworks including inference APIs, embedding services, classification services, and reusable AI microservices.Architect end-to-end AI systems covering data ingestion, feature engineering, model training, deployment, monitoring, and retraining.Establish scalable model serving infrastructure using modern AI deployment frameworks and cloud-native technologies.Define the long-term AI platform roadmap and technical strategy.Data Architecture & EngineeringDesign and implement enterprise-scale data lakehouse architecture utilizing cloud-native technologies and modern data platforms.Build and optimize batch and real-time data pipelines supporting AI and analytics workloads.Architect feature stores and feature management capabilities for machine learning applications.Establish data governance frameworks, metadata management, lineage tracking, and data catalog capabilities.Implement data quality controls, monitoring, and automated validation frameworks.Lead integration of internal and third-party data sources into a centralized AI-ready data ecosystem.Cloud & Infrastructure ArchitectureDesign highly available, scalable, and secure cloud infrastructure for AI workloads.Build containerized and Kubernetes-based AI platforms supporting training and inference environments.Define infrastructure-as-code standards and cloud governance frameworks.Architect cost-optimized cloud environments balancing performance, scalability, and operational efficiency.Establish enterprise-grade networking, storage, compute, and security architectures.MLOps & Model Lifecycle ManagementBuild and manage enterprise MLOps capabilities supporting model development, deployment, monitoring, and governace.Implement experiment tracking, model registry, versioning, and automated deployment pipelines.Establish model monitoring, drift detection, retraining, and performance management frameworks.Define CI/CD standards for machine learning and AI deployments.Implement robust rollback, testing, validation, and release management processes.Enterprise Solution ArchitectureDesign integration patterns connecting AI services with enterprise applications and business platforms.Develop API architectures and service contracts for AI products and platforms.Establish event-driven architectures supporting real-time AI applications.Define AI consumption models, governance standards, and cost attribution frameworks.Produce architecture documentation, technical standards, and architectural decision records.Security, Governance & ComplianceEmbed security, privacy, and compliance controls into all AI and data platforms.Implement identity management, access control, encryption, secrets management, and monitoring frameworks.Support regulatory and governance requirements across global operations.Establish AI governance frameworks covering risk management, transparency, model evaluation, and responsible AI practices.Ensure alignment with industry best practices and enterprise security standards.Leadership & Technical OwnershipAct as the senior technical authority for enterprise AI architecture.Define engineering standards, architectural principles, and development best practices.Mentor and develop AI engineers, data engineers, platform engineers, and DevOps professionals.Evaluate emerging technologies and provide strategic recommendations on platform evolution.Support hiring, team development, and capability-building initiatives as the AI function grows.RequiremntsBachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related field.15+ years of experience across software engineering, data engineering, cloud architecture, and AI/ML systems.Minimum 3–5 years in a senior architecture, principal engineer, or enterprise architecture capacity.Proven experience building and deploying production AI systems end-to-end.Extensive hands-on experience designing enterprise data platforms, data lakes, lakehouses, and feature stores.Deep expertise in AWS cloud architecture and large-scale distributed systems.Strong software engineering background with advanced Python and SQL development skills.Experience with modern AI/ML frameworks and machine learning infrastructure.Strong knowledge of Kubernetes, containerization, infrastructure automation, and platform engineering.Experience designing MLOps platforms and model lifecycle management frameworks.Strong understanding of data governance, security, compliance, and AI governance principles.Experience working within high-growth technology, fintech, financial services, SaaS, or enterprise software environments.Ability to communicate effectively with both technical teams and executive stakeholders.Proven experience mentoring engineers and establishing engineering standards. Preferred ExperienceExperience within Financial Services, FinTech, Trading, Payments, Banking, or Digital Assets environments.Experience building enterprise AI platforms supporting Generative AI and Large Language Model applications.Exposure to Databricks, SageMaker, MLflow, Kubeflow, Feast, OpenMetadata, Kafka, and modern AI infrastructure technologies.Experience operating in regulated environments and supporting global-scale platforms.