Visa Status: GC/citizen.(C2C)
Location: Remote or NYC area; New York working-hour alignment preferred
The Role
Interparfums is advancing its AI transformation as part of this evolving how work gets done across the
business. The Senior Director of AI owns the strategy, roadmap, and organizational transformation. The
AI Engineer will be her hands-on technical partner who turns selected priorities into working
prototypes, practical initial implementations, and clear technical recommendations.
This role calls for an engineer with practical experience applying AI-native ways of working where they
fit. They look across built-in application features, enterprise copilots, configurable agents, and custom
builds to find the best-fit approach for the AI need. They test what works in practice, identify
overlapping capabilities, compare cost and effort, and apply their own judgment before recommending
or building anything. They work within IP’s policies, tools, and operating environment.
Priorities will be set collaboratively and may shift as technical discovery, platform capabilities, and
business needs evolve.
What You’ll Do
• Build and prototype: Develop AI agents, copilots, and proofs of concept selected by IP as
business priorities evolve. An initial focus is expected to be a chatbot embedded in a Microsoft BI
dashboard built on IP’s Microsoft data warehouse.
• Work AI-natively: Apply emerging AI-assisted and agentic approaches to research, solution
design, context and tool configuration, rapid prototyping, evaluation, testing, and documentation.
• Integrate enterprise data: Connect AI experiences to governed data, semantic models, and
business applications using the appropriate interfaces, including APIs, platform connectors, and
MCP-based tools or resources, in line with access controls and data ownership.
• Evaluate through evidence: Assess where IP applicable AI-enabled applications, MS Copilot,
OpenAI, Claude capabilities through research and hands-on testing. Identify overlapping applications
capabilities, compare cost and implementation effort, and communicate practical adopt, wait, or skip
recommendations.
• Engineer responsibly: Apply pragmatic evaluation, least-privilege access, human oversight,
observability, and token or credit cost controls appropriate to each prototype or initial
implementation.
• Work with IP teams: Work alongside IP developers and automation builders, adapting to
different levels of AI familiarity, limited team capacity, and IP’s current working practices.
• Transition ownership: Build production-oriented prototypes and selected initial implementations,
document key decisions, and prepare solutions for ongoing ownership by IP staff.
Required Qualifications
• AI-native working practices: Practical experience applying AI to parts of the engineering
lifecycle. Can point to specific ways AI has changed how they research, prototype, build, test,
evaluate, or document solutions. Be thoughtful on approaches for a business is at different stages of
AI adoption.
• Hands-on AI engineering: Experience configuring or building embedded AI features, LLM
enabled applications, copilots, agents, or comparable AI solutions, with the engineering judgment to
select an appropriate approach as requirements become clearer.
• Microsoft ecosystem experience: Hands-on familiarity with Microsoft’s enterprise AI and data
environment, along with sound judgment about technical fit.
• Data and integration fluency: Strong working knowledge of SQL, governed enterprise data,
APIs, and system integration. Able to reason about how conversational AI should interact with
trusted business data.
• Responsible implementation: Working knowledge of AI evaluation, data security, identity and
least-privilege access, prompt-injection risk, human review, and usage-cost fundamentals.
• Communication and collaboration: Able to explain technical options and write concise findings
Clearly,
Preferred Qualifications
• Additional AI platforms: Practical familiarity with OpenAI/ChatGPT and Anthropic/Claude
ecosystems.
• Microsoft data and applications: Experience with Power BI, semantic models, Microsoft Fabric,
Dynamics 365 Business Central, and Microsoft CRM for Sales.
• Emerging development practices: Exposure to agent development lifecycle concepts such as
evaluation-driven development, context and tool design, model or prompt versioning, observability,
and controlled rollout. Able to update IP on latest Microsoft AI evolutions, including agent delivery
architectures like the Copilot Super app.
• Proactively facilitates knowledge transfer to the IT staff within the scope of assigned projects
when requested, focusing on AI-augmented development workflows and modern AI ways of
working through co-engineering, peer pairing and walk-throughs of active project deliverables.
• Technical evaluation: Experience testing AI vendor claims, comparing implementation options, or
translating a proof of concept into an evidence-based recommendation.