AI Manager

The Principal Data Scientist/AI Engineer is a senior technical expert who leads complex, high-impact AI initiatives across the organization. This role is responsible for driving architectural decisions, establishing reusable frameworks, and influencing the AI strategy at an enterprise level. The Principal AI Engineer operates as a cross-functional advisor, mentoring engineering teams, evaluating emerging technologies, and setting the long-term direction for AI systems at scale.

Key Responsibilities:

• Serve as the technical authority on all AI/ML and GenAI solutions across business lines Define and enforce AI security, ethical AI principles, and model governance guidelines in line with global standards
• Design and review architectures for GenAI, LLM integrations, and autonomous agent systems
• Develop reusable frameworks for MLOps, data pipelines, vector store integrations, and RAG-based systems Identify and integrate state-of-the-art AI capabilities, tools, and libraries through continuous research and experimentation Lead cross-functional technical reviews, influence design across product and engineering teams
• Mentor Lead and Senior Engineers on advanced system design, experimentation, and production deployment Represent AI Engineering in enterprise architectural councils or tech steering groups
• Build thought leadership through white papers, tech talks, and participation in AI communities

Qualifications:

• Masters or PhD in AI/ML, Computer Science, or related quantitative field 8+ years of experience in advanced AI/ML solutioning, including system-level design Proven track record of leading large-scale, production-grade AI initiatives
• Deep expertise in GenAI: OpenAI/Anthropic APIs, model customization, LangChain, vector DBs, orchestration frameworks
• Advanced knowledge of cloud AI stacks (Azure preferred), data engineering, containerization, and MLOps workflows Strong understanding of security, privacy, explainability, and compliance in AI/ML solutions

Preferred Qualifications:

• Experience in defining enterprise-wide AI/ML standards and frameworks Participation in AI patent filings, publications, or open-source contributions
• Ability to influence non-technical leaders and translate technical depth into strategic direction