Senior AI Architect

Job Duties And Responsibilities

• Partner with the AI Portfolio & Product Manager, business leaders, operations leaders, product owners, and subject matter experts to understand current-state workflows, pain points, decision points, system constraints, data availability, integration needs, and desired business outcomes.
• Assess prioritized or emerging AI opportunities for technical feasibility, data readiness, architecture implications, integration complexity, security, governance, operational support, and delivery risk.
• Translate business context into technical assumptions, solution options, architectural tradeoffs, implementation considerations, and readiness recommendations.
• Shape practical AI-enabled workflow concepts that move work from manual execution, rules-heavy processes, and exception-driven operations toward intelligent systems with human oversight, feedback loops, and continuous improvement.
• Define enterprise-grade architecture for AI-enabled solutions, including business process fit, data needs, AI/model approach, system interactions, integration patterns, security, human oversight, monitoring, and operational support.
• Create solution artifacts such as target-state workflows, context diagrams, data flows, decision flows, integration designs, and architecture decision records.
• Evaluate and recommend appropriate AI and automation solution patterns, including traditional machine learning, predictive models, optimization, generative AI, retrieval-augmented generation, agentic workflows, workflow automation, rules-based components, or hybrid approaches based on business need, data readiness, feasibility, risk, scalability, and maintainability.
• Design solutions for scalability, reliability, observability, privacy, compliance, supportability, responsible AI guardrails, and long-term operational ownership.
• Collaborate with enterprise architecture, data architecture, security, compliance, and governance stakeholders to align AI solutions with enterprise standards and delivery expectations.
• Build or directly contribute to proofs-of-concept, prototypes, technical spikes, and reference implementations to validate feasibility, test assumptions, compare approaches, and de-risk delivery.
• Translate architecture decisions into practical implementation guidance, reusable patterns, sample components, and working examples that AI Engineers and delivery partners can build from.
• Evaluate AI services, frameworks, platforms, orchestration patterns, model evaluation approaches, vector databases, integration approaches, and automation tools where needed to establish practical, reusable solution patterns.
• Support early implementation, design reviews, code reviews, and complex troubleshooting when ambiguity, integration complexity, model behavior, security, responsible AI requirements, or emerging AI capabilities require senior technical judgment.
• Mentor engineers, analysts, and business partners through hands-on collaboration, technical coaching, and practical decision support.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, or a related technical discipline required. Master’s degree preferred.
• 10+ years of software engineering or architecture experience, with at least 5 years in AI/ML architecture and solution leadership.
• Practical experience with LLM deployment, vector databases, RAG architecture, or similar emerging AI capabilities, cycle management, MLOps, and cloud-native deployments.
• Strong expertise with platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, and OpenAI APIs.
• Demonstrated experience leading cross-functional teams and influencing enterprise-wide architecture decisions.
• Prior experience contributing to AI governance frameworks or responsible AI initiatives.
• Familiarity with enterprise security, data privacy laws, and risk management practices related to AI.
• Enterprise architecture certification (e.g., TOGAF, Zachman) is a plus.
• Strong organizational skills and attention to detail.
• Relevant certifications such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, or similar credentials are preferred but not required.

Benefits

Our comprehensive benefits include: medical, dental and vision insurance coverage; 100% company-paid life and disability coverage, 401k options with company match, three weeks PTO by the end of the first year and much more.