AI Development Engineer - Onshore

Pragma Edge Inc; New York (NY); Remote; USD 180,000 - 260,000; Full time

Job summary

Interparfums is accelerating its AI transformation. The Senior Director of AI owns strategy, roadmap, and organizational change, working with engineers to turn priorities into prototypes and practical implementations.

The AI Engineer partner applies AI-native methods across research, design, and deployment, evaluating evolving tools and integrating enterprise data, APIs, and governance. This role emphasizes pragmatic, secure, and cost-aware solutions within IP’s environment.

Qualifications

• AI-native practices across the engineering lifecycle.
• Hands-on AI engineering with embedded features and copilots.
• Microsoft ecosystem experience in enterprise AI and data.
• Data and integration fluency with SQL, governed enterprise data, APIs, and system integration.
• Responsible implementation including data security, identity, and cost controls.
• Clear communication of technical options and findings.

Responsibilities

• Build AI agents, copilots, and proofs of concept.
• Prototype AI-native solutions for dashboards and data warehouses.
• Integrate AI experiences with governed data and APIs.
• Evaluate capabilities across OpenAI/Claude/MS Copilot and related tools.
• Document key decisions and prepare ownership transfer to IP staff.
• Collaborate with IP developers and automation teams.

Skills

AI-native practices

Hands-on AI engineering

Microsoft ecosystem experience

Data and integration fluency

Responsible implementation

Communication and collaboration

Tools

Power BI

SQL

APIs

Job description

New york, United States | Posted on 09/11/2026

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
• 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.