Senior Architect, Agentic AI for Marketing

What you'll do

• Lead the architecture and delivery of Agentic AI solutions for personalization, content discovery, and workflow automation.
• Translate marketing business needs into practical agent architectures including goals, tools, memory, and planning.
• Build and advance platform capabilities like multi-agent orchestration, recommendation APIs, and embedding pipelines.
• Shape the platform roadmap for agent registries, permission models, evaluation frameworks, and lifecycle management.
• Design reliable interfaces for agent invocation, tool execution, and integration with enterprise analytics systems.
• Establish production practices for evaluation, regression testing, observability, latency, cost, and safety.
• Guide technical tradeoffs between model quality, retrieval precision, inference cost, and business impact.
• Build prototypes, reference architectures, and reusable components to move ideas into scalable production systems.

What we're looking for

• A BS, MS, or PhD in Computer Science, AI/ML, Electrical Engineering, Data Science, or a related technical field, or equivalent experience.
• 12+ years of experience in software engineering, AI/ML engineering, solutions architecture, applied AI, data platforms, or large-scale production systems.
• Experience building and deploying applications involving LLMs, generative AI, RAG, recommendation systems, conversational AI, or agentic AI.
• Strong programming skills in Python with experience in APIs, Linux, distributed systems, containers, cloud-native infrastructure, and production debugging.
• Understanding of agentic AI system design, including tool use, orchestration, planning, memory, retrieval, evaluation, guardrails, and failure handling.
• Experience designing integrations between AI agents and enterprise tools using MCP, function calling, API gateways, or related interoperability patterns.
• Experience developing conversational AI experiences grounded in structured or semi-structured data, including text-to-SQL and multi-turn dialogue.
• Experience with production AI or software infrastructure, including model serving, Kubernetes, Docker, CI/CD, observability, and cost optimization.