Associate Director of Artificial Intelligence Research

Associate Director – AI Research

Company Description

WNS, part of Capgemini, is an Agentic AI-powered leader in intelligent operations and transformation, serving more than 700 clients across 10 industries, including Banking and Financial Services, Healthcare, Insurance, Shipping and Logistics, and Travel and Hospitality. We bring together deep domain excellence – WNS’ core differentiator – with AI-powered platforms and analytics to help businesses innovate, scale, adapt and build resilience in a world defined by disruption.

Our purpose is clear: to enable lasting business value by designing intelligent, human-led solutions that deliver sustainable outcomes and a differentiated impact. With three global headquarters across four continents, operations in 13 countries, 65 delivery centers and more than 66,000 employees, WNS combines scale, expertise and execution to create meaningful, measurable impact.

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Qualifications

Role Overview:

The Associate Director of AI Research is a strategic leadership role responsible for defining and driving the enterprise innovation roadmap for artificial intelligence. With 9–15 years of experience in applied AI and machine learning, this role focuses on directing the research, evaluation, and scaling of advanced methodologies across generative AI, foundational models, and complex agentic systems.

The individual is expected to serve as a visionary leader within the rapidly evolving AI ecosystem, anticipating market shifts and determining the strategic feasibility of adopting cutting-edge technologies. Operating as a bridge between high-level executive objectives and deep technical execution, the Associate Director plays a crucial role in transforming market innovation into tangible, enterprise-wide capabilities.

The ideal candidate combines exceptional business acumen with deep architectural expertise in AI systems. They must be capable of leading high-performing research teams, steering multi-million-dollar technology investments, and communicating complex technical paradigms to C-suite stakeholders to secure buy-in for transformative AI initiatives.

Responsibilities:

• Continuously monitor and evaluate emerging developments in artificial intelligence, including advancements in foundational models, generative AI, multi-modal AI, and agentic systems.
• Conduct structured research and experimentation to assess the performance, feasibility, and enterprise applicability of new AI models, frameworks, and architectures.
• Analyze competing technologies and approaches to determine optimal AI solutions and implementation strategies for business use cases.
• Develop research prototypes and proof-of-concepts to validate new AI capabilities before enterprise adoption.
• Provide technical recommendations and research insights to leadership and engineering teams regarding emerging AI opportunities.
• Translate research findings into practical guidance, reference architecture, and implementation frameworks for downstream teams.
• Collaborate with engineering, data science, and product teams to support the adoption of validated AI capabilities.
• Evaluate open-source and commercial AI ecosystems, identifying tools, models, and platforms that can accelerate enterprise AI capabilities.
• Document research findings, benchmarking results, and architectural recommendations to support informed decision-making.
• Mentor researchers, managers and contribute to building a strong internal knowledge base around evolving AI technologies.

Skills:

• Expert-level understanding of LLM ecosystems, generative AI, Agentic Architectures, multi-modal architectures, and advanced reasoning methodologies
• Deep strategic knowledge of Agentic systems, including planning agents, orchestration layers, and complex decision-making frameworks
• Exceptional business acumen required to translate highly technical AI research into compelling business cases and measurable ROI
• Executive presence and outstanding communication skills necessary to influence cross-functional leadership and board-level stakeholders
• Proven ability to design scalable, secure, and cost-effective AI system architectures for enterprise deployment
• Comprehensive familiarity with cloud-based AI ecosystems, enterprise integration patterns, and modern data platforms
• Strategic understanding of comprehensive AI evaluation frameworks, rubric-based scoring, and algorithmic bias mitigation
• Demonstrated leadership experience in building, mentoring, and managing high-performing technical research and engineering teams

Additional Information

Qualifications & Educational Requirements:

• Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field.
• 9 to 15 years of progressive experience in AI research, data science, or machine learning engineering.
• Proven track record of successfully guiding complex AI research projects from conceptual prototypes to enterprise-scale production
• Extensive experience navigating the complexities of corporate AI governance, security compliance, and data privacy regulations
• Published research, patents, or recognized thought leadership contributions to the broader AI community are highly preferred
• Demonstrated ability to manage departmental budgets, negotiate vendor contracts, and optimize technology spend
• Deep capability to thrive in highly ambiguous, fast-moving technological landscapes while providing clear direction to teams

Experience Level

Mid Level