Own the company’s AI roadmap end-to-end — from foundation model selection to business-unit-specific deployment plans:
Define and continuously refine the enterprise AI strategy, aligning it to revenue goals, product differentiation, and operational efficiency targets. - Conduct rigorous build-vs-buy-vs-partner analysis for foundation models, AI tooling, inference infrastructure, and data platforms. - Establish an AI governance framework covering model risk, data privacy, bias mitigation, and regulatory compliance across jurisdictions. - Serve as the primary AI advisor to the CEO and executive leadership team; translate frontier AI developments into actionable business implications. - Build and maintain a rolling 6/12/24-month AI transformation roadmap with clear milestones, investment thresholds, and go/no-go decision points. - Identify and evaluate strategic AI acquisition, investment, and partnership opportunities. ### 2. System Building & Technical Execution
Architect and deliver production-grade AI systems that create measurable business impact — not just prototypes:
Lead the architecture of LLM-powered applications including RAG systems, agentic workflows, fine-tuning pipelines, and prompt engineering frameworks at enterprise scale. - Design and implement AI-native infrastructure: model serving, automated evaluation, A/B testing frameworks, version control for prompts and models, and continuous monitoring for quality and drift. - Build and optimize AI agent systems, multi-model orchestration, tool-use chains, and autonomous workflow engines that solve real business problems end-to-end. - Build robust evaluation and benchmarking systems for AI outputs — measuring hallucination rates, task completion accuracy, latency, safety, and end-user satisfaction. - Personally prototype and review critical AI system designs; maintain hands-on technical credibility with the engineering team. ###
3. Organizational AI Enablement & Adoption
Drive AI adoption across every business unit — making AI a core competency for the entire organization, not just the engineering team:
Design and execute a company-wide AI literacy program segmented by role: executive leadership, product managers, engineers, operations, customer-facing teams, and support functions. - Create internal AI tooling, templates, and playbooks that make it radically easy for every business unit to leverage AI capabilities (prompt libraries, no-code/low-code AI interfaces, internal copilots, AI-assisted workflows). - Establish an AI Center of Excellence that serves as the hub for best practices, reusable components, and cross-functional AI project incubation. - Implement a structured AI use-case intake and prioritization process: partner with each business unit to identify high-ROI AI opportunities, scope them properly, and execute with embedded AI support. - Build an AI talent strategy: define hiring profiles for AI engineers and applied AI roles, design technical interview processes, and develop retention programs for top AI talent. - Foster a culture of responsible AI experimentation: psychological safety to try and fail fast, coupled with rigorous post-mortems and knowledge sharing across BUs. ## What We Look For In You
10+ years in AI / deep learning, with at least 5 years in a senior leadership role (Director+ or equivalent at a top-tier tech company, high-growth startup, or leading AI lab). - Demonstrated track record of shipping production AI systems that directly impacted business outcomes at scale (revenue, engagement, efficiency). - Deep expertise across the modern AI stack: large language models, transformer architectures, RAG, fine-tuning, RLHF/DPO, prompt engineering, and agentic AI frameworks. - Strong publication record, open-source contributions, or recognized thought leadership in the AI community (conference talks, technical blog posts, courses, or widely-adopted tools). - Exceptional communication skills: ability to present complex technical concepts to board-level audiences and translate business needs into technical specifications. - Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field. ##
Nice to Haves
Experience in fintech, crypto/blockchain, or regulated industries with complex compliance requirements. - Hands-on experience building AI-native products in consumer-facing, high-throughput environments (millions of daily active users). - Track record of driving company-wide AI adoption initiatives — not just team-level, but organization-wide cultural and process transformation. - Experience building or contributing to open-source AI tools, frameworks, or educational content adopted by the broader community. - Familiarity with AI regulatory landscapes across multiple jurisdictions (US, EU AI Act, APAC frameworks). - Published author or recognized educator in AI (books, widely-read technical blogs, MOOCs, or workshops). ##
Perks & Benefits
Competitive total compensation package
L&D programs and Education subsidy for employees' growth and development
Various team building programs and company events
Wellness and meal allowances
Comprehensive healthcare schemes for employees and dependants
More that we love to tell you along the process! #LI-VW1 #ONSITE
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