Principal Architect

Primary Responsibilities:
Define and own the AI/ML architecture roadmap, ensuring alignment with enterprise technology strategy and business objectives
Lead the design of end-to-end AI solutions including data pipelines, model training, inference infrastructure, and MLOps frameworks
Serve as a technical authority and mentor for AI/ML engineers, tech leads, and solution architects across the organization
Collaborate with product managers, data scientists, and engineering teams to translate business requirements into robust architectural designs
Evaluate and recommend AI/ML platforms, tools, and frameworks (e.g., LLMs, RAG pipelines, agent frameworks, vector databases)
Drive adoption of responsible AI principles including explainability, fairness, bias mitigation, and model governance
Partner with security, compliance, and infrastructure teams to ensure AI solutions meet regulatory and enterprise standards (HIPAA, SOC2, etc.)
Lead architecture reviews, proof-of-concepts, and technical spike initiatives
Represent the AI architecture function in cross-functional leadership discussions and executive briefings

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Required Qualifications:
8+ years of overall software/systems engineering experience, with 5+ years focused on AI/ML architecture
5+ years of experience owning and defining the AI/ML architecture roadmap, aligned to enterprise technology strategy and business objectives
5+ years of experience design end-to-end AI solutions spanning data pipelines, model training, inference infrastructure, and MLOps frameworks
5+ years of hands-on experience with MLOps: CI/CD for ML, model monitoring, drift detection, and model lifecycle management
Deep expertise in machine learning, deep learning, NLP, LLMs, and generative AI technologies
Proven solid proficiency in cloud platforms - AWS, Azure, or GCP - and cloud-native AI/ML services
Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain)
Proven solid understanding of data architecture, data engineering, and real-time/batch processing pipelines

Preferred Qualifications:
Demonstrated partnership with security, compliance, and infrastructure teams to ensure AI solutions meet regulatory and enterprise standards (HIPAA, SOC2, etc.)
Experience with RAG pipelines, agent frameworks, and vector databases at scale
Demonstrated familiarity with responsible AI principles: explainability, fairness, bias mitigation, and model governance
Experience representing AI architecture in executive briefings and cross-functional leadership forums
Experience as a technical mentor for AI/ML engineers, tech leads, and solution architects across the organization
Proven excellent communication skills - ability to influence and align both technical and non-technical audiences