AI Architect

AI Architect

Role Summary

The AI Architect is responsible for designing the end-to-end architecture, frameworks that enable scalable, and high-performance AI systems both within the organization and for product teams. This role bridges machine learning, software engineering, and cloud infrastructure to create a cohesive enterprise AI ecosystem. The AI Architect defines reference architectures, accelerates solution teams, ensures compliance, and sets the technical direction for how AI is built, deployed, and governed.

Key Responsibilities

• Design the AI architecture for the enterprise including inference layers, vector stores, data ingestion, orchestration, and monitoring.
• Architect scalable LLM/RAG systems, agent frameworks, and generative AI services that can be reused across domains and business units.
• Define standards for embeddings, vectorization, prompt orchestration, caching layers, and evaluation pipelines.
• Establish patterns for developing, fine-tuning, and deploying ML/LLM models
• Evaluate when to use foundation models, when to fine-tune, and when to build custom models.
• Define and enforce AI architecture principles, security policies, and compliance (HIPAA, FDA, ISO).
• Implement guardrails for privacy, PHI/PII protection, safe model usage, hallucination risk mitigation, audit logging, and explainability.
• Partner with data engineering, IT security, cloud infrastructure, and product teams to ensure architectural alignment.
• Participate in roadmap planning and technology selection for the AI/ML ecosystem.
• Conduct build-vs-buy assessments for AI platforms, tokenization, data protection, vector databases, model hosting, and MLOps tools.

Required Qualifications

• Bachelor's or Master's in Computer Science, Engineering, AI/ML, or related field; equivalent experience considered.
• 5+ years of experience in ML/AI engineering, data engineering, platform engineering, or cloud architecture.
• Strong proficiency in distributed systems, cloud architecture (Azure), and containerization (Kubernetes).
• Hands-on experience designing and deploying ML/LLM systems in production.
• Expertise with ML frameworks (PyTorch, TensorFlow), MLOps tools (MLflow, KServe, Kubeflow, Airflow), and vector databases.
• Deep understanding of LLM/RAG patterns, embeddings, prompt engineering, caching layers, and model evaluation.

Preferred Qualifications

• Experience with agent frameworks (LangChain, OpenAI Agents API).
• Experience in highly regulated industries (healthcare, MedTech, pharma).
• Experience with encryption, tokenization, PHI/PII protection, or secure ML workflows.