Applied AI Architect

This is a fully remote position, open to applicants in New York.

📋 Description

• Design target architectures and scalable data pipelines for enterprise AI systems.

• Architect ETL/ELT processes, feature stores, vector databases, knowledge layers, and AI data pipelines.

• Assess and choose AI models based on specific use cases, performance metrics, costs, latency, security, and governance needs.

• Create AI solutions that integrate with electronic health records (EHRs), core banking systems, CRM platforms, data lakes, and data warehouses.

• Define and implement frameworks for AI governance, compliance, privacy, and responsible AI practices.

• Convert regulatory requirements into actionable technical and architectural controls.

• Incorporate security, privacy, identity and access management (IAM), and data residency considerations.

• Design production environments utilizing Azure AI Foundry, AWS Bedrock, and Google Vertex AI.

• Collaborate with AI Builders and Value Engineers throughout the phases from discovery to production.

• Lay the groundwork for MLOps, continuous integration/continuous deployment (CI/CD), model lifecycle management, observability, monitoring, and evaluation.

• Transition AI solutions from proof-of-concept stages into secure and scalable production environments.

⛳️ Requirements

• Extensive experience as an AI, ML, Data, Solutions, or Enterprise Architect, with practical experience in designing and deploying AI solutions.

• Deep understanding of Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), AI orchestration, and contemporary AI architectures.

• Proven experience in designing enterprise data architectures, pipelines, integrations, and scalable AI platforms.

• Familiarity with evaluating AI models and recognizing trade-offs between commercial and open-source models, as well as RAG versus fine-tuning.

• Proficiency with Azure AI Foundry, AWS Bedrock, Google Vertex AI, LangGraph, and LangChain.

• Experience in integrating AI with enterprise systems and APIs.

• Strong grasp of AI governance, model risk management, responsible AI, privacy, security, and regulatory compliance.

• Background working in regulated environments; familiarity with HIPAA, GxP/CSV, AML, Basel III, or SR 11-7 is highly desirable.

• Knowledge of PHI/PII governance, data residency, VPC deployment, IAM, access control, and model inference security.

• Experience in production AI engineering, encompassing CI/CD, MLOps, model registries, monitoring, observability, and evaluation frameworks.

• Excellent communication skills with the ability to translate intricate technical, business, security, and regulatory requirements into clear architectural choices.

🏝️ Benefits

• Recognized as a Great Place to Work®.

• Opportunity to engage with cutting-edge technologies.

• Chance to tackle complex, high-impact business challenges.