Overview:

The Opportunity

As Senior Director in the Pre Sales organization covering AI Natives & Inference, you will lead the technical engagement strategy for G42 with AI-native companies building the next generation of applications, agents, model platforms, and inference-driven services. You will shape how G42 brings high-performance AI infrastructure, inference capacity, and platform capabilities to the companies defining the future of AI adoption.

This role sits at the intersection of AI infrastructure, customer strategy, and commercial execution, with a focus on scaling inference workloads and accelerating adoption among AI-native innovators. You will identify market opportunities, build trusted relationships with founders and technology leaders, and translate customer requirements into scalable offerings that help G42 become a preferred partner for AI inference at global scale.

Responsibilities:

• Define and lead the strategy for engaging AI-native companies and scaling inference adoption within G42’s AI ecosystem.
• Design commercial models that enable scalable access to AI infrastructure, inference capacity, agents, and platforms.
• Develop customer segmentation, workload prioritization, and growth plans to drive adoption among AI-native innovators.
• Partner with product, engineering, and GTM teams to translate inference needs into differentiated platform offerings.
• Evaluate performance, security, reliability, and governance considerations for production inference environments.
• Build frameworks for pricing, capacity planning, and revenue models across inference-driven AI services.
• Engage with strategic customers, founders, and ecosystem partners to accelerate adoption of G42 inference capabilities.
• Stay ahead of market trends in AI-native applications, inference infrastructure, and AI platform monetization strategies.

Skills & Attributes for Success

• AI infrastructure and market expertise: Strong understanding of AI-native ecosystems, inference platforms, GPU infrastructure economics, and platform monetization models.
• Customer and commercial acumen: Ability to translate customer workloads, performance requirements, and growth plans into compelling technical solutions and scalable commercial models.
• Strategic execution: Capacity to set a clear direction and convert it into practical priorities, measurable outcomes, and disciplined execution in fast-moving AI markets.
• Executive communication: Skilled at simplifying complex technical and commercial concepts for founders, technology leaders, partners, and senior executives.
• Cross-functional leadership: Proven ability to align product, engineering, sales, finance, and operations teams around customer needs and shared business objectives.
• Ownership and adaptability: Comfortable operating with ambiguity, making informed decisions at pace, and taking accountability for customer and commercial outcomes.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Business, Economics, or related field.
• 12–18 years of experience in AI infrastructure, platform strategy, cloud services, or enterprise technology.
• Proven experience building or scaling commercial models for AI platforms, inference services, or cloud infrastructure.
• Strong understanding of GPU infrastructure, inference workloads, AI platforms, or high-performance cloud services.
• Experience working in complex, cross-functional environments with product and engineering teams.

Ideally, You’ll Also Have

• AI-native company experience: Direct experience supporting foundation model companies, AI application providers, agentic AI platforms, or inference-first startups as they move from development to production scale.
• Production inference expertise: Familiarity with model serving, inference optimization, latency and throughput trade-offs, workload orchestration, and high-availability production environments.
• GPU platform and unit economics knowledge: Understanding of accelerated compute platforms, GPU utilization, capacity planning, cost per token or request, and the economics of scaling inference services.
• AI software ecosystem exposure: Experience with model APIs, inference engines, serving frameworks, vector databases, observability tools, or other components of modern AI application stacks.
• Founder and ecosystem engagement: Ability to engage credibly with founders and technical leaders, shape early solution requirements, and build partnerships across cloud, model, tooling, and AI developer ecosystems.

What we look for

At G42, we look for people who share our ambition to build a future to believe in. You see opportunities in complexity and move with pace to turn vision into reality. You think big, act decisively, and embrace AI as a catalyst for progress. You build trust through collaboration, seek out diverse perspectives, and turn ideas into tangible impact. You challenge assumptions, simplify the complex, and work with intention. Above all, you take ownership for outcomes, for growth, and for shaping solutions that matter to our customers, communities, and the world.