AI Architect

Orrick Herrington & Sutcliffe LLP•Any US Office, UNAVAILABLE

2d•Remote

About The Position

Orrick currently has an excellent opportunity for an AI Architect. This position could be based in any of our U.S. offices and consideration given for 100% remote US locations. The AI Architect is responsible for the engineering of AI solutions (including their scaling and operationalization) through the use of appropriate methodologies, frameworks, tools, and platforms. This role ensures the re-use, integration, performance, quality, and manageability of these solutions through coordination with other technology architects, data architects and enterprise architects.

Requirements

A master’s or Ph.D. in computer engineering, computer science, software engineering, artificial intelligence, mathematics or related field in the natural science
A minimum of five years of proven experience in AI/ML development and application, with at least two years focused on designing enterprise-scale AI architecture and system integration
A minimum of five years of experience developing tools or platforms in a software engineering role
Proven experience working with language models while designing for cost efficiency and scalability
Skilled in selecting and architecting AI frameworks (e.g., TensorFlow, PyTorch, LangChain, Hugging Face), cloud platforms (Azure, AWS, GCP), and orchestration tools (Docker, Kubernetes) for scalable enterprise AI solutions
Experience designing agentic AI solutions, including AI agent orchestration, tool integration, workflow automation, memory/context management, and secure access to enterprise systems
Experience with Model Context Protocol (MCP) or similar frameworks for integrating AI agents with enterprise systems and data sources
Familiarity with ModelOps, AI engineering practices, DevOps and MLOps (e.g., CI/CD pipelines)
Experience defining monitoring, evaluation, and optimization strategies for AI systems, including performance, reliability, cost efficiency, model quality, and user feedback loops
Experience architecting and implementing generative AI solutions utilizing Azure AI Foundry, large language models (LLMs), NLP, vector databases, knowledge graphs, retrieval-augmented generation (RAG), and agentic AI architectures
Advanced understanding of system integrations architecture
Ability to apply AI application testing techniques (e.g., pairwise testing, A/B testing, back-to-back testing) across operating system platforms
Experience architecting AI systems that comply with legal data boundaries, ensuring zero data leakage, rigorous access controls, and absolute isolation of client data
Excellent written and verbal communication skills; ability to communicate complex AI concepts to non-technical audiences
Strong documentation skills with the ability to produce diagrams, demos and technical artifacts that make AI architectures comprehensible and actionable

Nice To Haves

Industry-recognized certifications in artificial intelligence, machine learning, generative AI, cloud-based AI platforms, or related technologies preferred

Responsibilities

Designs end-to-end AI products to address specific business needs and provides implementation guidance upon delivery
Provides expertise in creating and implementing enterprise architecture solutions that enhance AI capabilities
Navigates the technical complexity required to meet business requirements (e.g., by developing scenarios or positions on how AI platform, framework or tool capabilities are selected and complement one another)
Architects AI platforms and guides decision making for choosing the right framework, governance and operational processes to ensure safety and security at scale
Ensures seamless integration of AI solutions from development to business operations within the organization’s enterprise architecture framework
Participates in evaluation of AI vendors, platforms, and third-party services, including assessment of architecture fit, security posture, integration requirements, scalability, and governance alignment
Partners with Legal, IT, Innovation, HR, Risk, and business stakeholders to assess, document, and mitigate risks associated with AI solution design, deployment, and adoption
Streamlines collaboration by coordinating the efforts of cross-functional participants to identify and prioritize requirements