Senior AI Architect

Company Overview

Role Overview

Senior AI Architec

AI Platform Architecture

• Design 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 & Engineering

• Design 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 workloa
• ds.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 Architecture

• Design 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 Management

• Build 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 Architecture

• Design 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 & Compliance

• Embed 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 Ownership

• Act 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
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Requiremnts

• Bachelor'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 Experience

• Experience 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.