Principal AI Architect

Overview
The Principal AI Architect is a senior technical leadership role responsible for defining and governing enterprise-wide AI, GenAI, and MLOps architecture. This role sets the long-term vision, reference architectures, and reusable patterns for scalable, secure, and responsible AI adoption across the organization. This role combines strong hands-on engineering with strategic innovation to design, prototype, and deliver intelligent, data-driven solutions that power analytics, machine learning, and next-generation AI applications.

Responsibilities
Enterprise AI & GenAI Architecture

• Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, agentic systems, and multimodal solutions.
• Establish standardized architectural patterns for model serving, prompt management, orchestration, tool use, and agent frameworks.
• Lead architecture decisions for buy vs. build, model selection, hosting strategies, and vendor integrations.
• Ensure AI architectures align with enterprise standards, cloud strategy, security, and governance.

MLOps & AI Platform Engineering

• Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.
• Architect end-to-endMLOpscapabilities, including model lifecycle management, CI/CD for ML, feature stores, model monitoring, and drift detection.
• Define enterprise patterns for training, fine-tuning, deployment, and observability of ML and GenAI workloads.
• Guide teams on productionizing PoCs into scalable, resilient, and supportable AI systems.
• Partner with platform teams to evolve a shared enterprise AI platform.

AWS-Centric AI Architecture

• Design AI and GenAI solutions using AWS-native services, including (but not limited to):
• Amazon Bedrock, SageMaker, Lambda, ECS/EKS
• S3, DynamoDB, Aurora, OpenSearch
• IAM, KMS, VPC, CloudWatch
• Define cost, performance, and scalability guardrails for AI workloads on AWS.
• Ensure architectures follow Well-Architected Framework principles.

Governance, Risk, and Responsible AI

• Partner with security, legal, and compliance teams to define AI governance, guardrails, and controls.
• Embed responsible AI principles, data privacy, and explainability into enterprise designs.
• Establish standards for model access, auditability, and risk management.

Technical Leadership & Influence

• Act as a principal-level advisor to senior technology and business leaders.
• Champion hands-on experimentation and rapid solution delivery whilemaintainingtechnical excellence.
• Mentor architects and senior engineers on AI architecture and MLOps best practices.
• Drive alignment across teams by publishing reference architectures, design standards, and decision frameworks.
• Represent the organization in architecture forums, reviews, and strategic initiatives.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field

• 10+ years of experience in enterprise architecture, data platforms, or distributed systems.

• Deep expertise in AI/ML architecture, including GenAI and LLM-based systems.
• Strong experience designing MLOps platforms and enterprise AI foundations.
• Proven experience architecting solutions on AWS.
• Strong understanding of cloud security, networking, and governance.
• Proficiency in Python, SQL, and modern data frameworks (e.g., Databricks, Airflow, Snowflake, Vertex AI).

Preferred

• Experience with agentic AI design patterns, including tool-use orchestration, autonomous workflow agents, or AI copilots.
• Proficiency in API design, microservices, and containerization (Docker, Kubernetes).
• Demonstrated ability to rapidly prototype new AI concepts and transition successful PoCs into production-grade systems.

Benefits are an integral part of total rewards and First Citizens