Senior Manager - Artificial Intelligence

We are seeking a forward-thinking Senior Manager of Artificial Intelligence who has successfully transitioned beyond traditional machine learning to architect and deploy modern Generative AI and Agentic systems. You will not just be managing models; you will be building autonomous ecosystems. We need a hands-on leader who has a proven track record of taking complex LLM-driven solutions from conceptual wrappers to robust, production-grade agentic workflows that drive measurable enterprise value.

What You’ll Do

• Drive Next-Gen Strategy: Own the enterprise AI roadmap with a heavy emphasis on Generative AI, autonomous agents, and applied Large Language Models.
• Orchestrate Agentic Ecosystems: Lead the design and deployment of multi-agent systems, orchestrating specialized AI agents (e.g., intelligence routing, operational execution, and system coding tasks) governed by centralized monitoring dashboards.
• Ship to Production: Guide cross-functional engineering teams to move GenAI proofs-of-concept into reliable, scalable, and secure production environments.
• Champion LLMOps: Establish the infrastructure required for modern AI, including advanced RAG (Retrieval-Augmented Generation) pipelines, vector databases, and continuous agent evaluation.
• Ensure AI Governance: Implement strict guardrails for Generative AI, focusing on algorithmic transparency, data privacy, security, and prompt injection mitigation.

What You Bring

• Background: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related highly technical field.
• Experience: 15+ years in technology or engineering roles, with recent, dedicated leadership experience explicitly focused on deploying Generative AI and Agentic solutions into production.
• Modern AI Expertise: Deep, practical knowledge of LLM architectures, fine-tuning methodologies, and multi-agent orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
• Infrastructure Acumen: Strong understanding of the modern AI stack, including vector databases, cloud-native deployments, and LLMOps practices.
• Leadership: Proven ability to demystify complex GenAI concepts for executive stakeholders, secure buy-in, and mentor high-performing teams of modern AI engineers.