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Team Overview
The AI Engineering and Platforms team designs, builds, and operates the foundational AI capabilities that support products and use cases for internal and external users across multi-cloud and hybrid environments. The team partners with Product and Engineering to deliver scalable, secure, and reliable AI platforms and AI-enabled solutions. The Senior Architect, AI Engineering and Platforms will report to Director for Software Development and is a highly technical, hands-on individual contributor responsible for designing, building, and operating core AI platform components.
This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.
Role Overview and Core Responsibilities
Contribute to the technical vision for foundational AI platforms and lead the architecture and delivery of assigned platform capabilities.
Design and implement scalable AI capabilities across multi-cloud and hybrid environments.
Implement and evolve the standardized development path for AI-enabled products, including agentic AI capabilities, observability, evaluations and measurement, operational controls, and guardrails.
Ensure the availability, performance, scalability, and security of assigned platform components.
Partner with cross-functional teams to translate business and technical requirements into platform designs and delivery plans.
Evaluate, prototype, and integrate emerging technologies that enhance platform capabilities and user outcomes.
Apply best practices for data governance, metadata management, data quality, security, and compliance.
Provide technical guidance, conduct design reviews, and mentor engineers.
Advance automation and DevOps practices supporting the continuous integration and deployment of data and AI services.
Required Knowledge and Experiences
Bachelor’s or master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience supporting the design and delivery of complex AI, data, or platform solutions.
10+ years of experience across ML (Machine Learning) engineering, data engineering, distributed systems, or platform development, including three or more years of hands-on experience building GenAI (Generative Artificial Intelligence) platforms or capabilities.
Hands-on experience delivering production GenAI solutions for internal or external users, including retrieval quality, latency management, cost optimization, and evaluation-driven quality improvements.
Experience building scalable AI or data platform components adopted by multiple teams, with measurable developer or user experience outcomes.
Strong problem-solving, analytical, communication, and collaboration skills, with the ability to explain complex technical concepts to technical and nontechnical audiences.
Required Technical Skills
Advanced expertise in at least one major cloud platform, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), with working knowledge of multi-cloud and hybrid architectures.
Strong proficiency with Docker, Kubernetes, infrastructure-as-code, API design, microservices architecture, continuous integration, and continuous deployment.
Hands-on knowledge of GenAI architecture and engineering, including vector search, knowledge graphs, Retrieval-Augmented Generation (RAG), agentic frameworks, model fine-tuning, evaluation frameworks, and cost management.
Experience with distributed computing technologies such as Apache Spark or Apache Hadoop and infrastructure-as-code tools such as Terraform or AWS CloudFormation.
Experience implementing cloud security and compliance controls and using observability tools such as Prometheus, Grafana, and the Elasticsearch, Logstash, and Kibana (ELK) Stack.
We’re also looking for the preferred skills below. Whether you…