Group Manager - AI Research

WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.

• 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 junior researchers and contribute to building a strong internal knowledge base around evolving AI technologies.

Key Skills Required:

• Strong understanding of modern AI and machine learning techniques, including large language models, generative AI, and emerging AI architectures.
• Ability to quickly analyze and understand new AI research papers, frameworks, and model releases, and evaluate their practical relevance.
• Experience working with AI ecosystems such as Hugging Face, Ollama, OpenAI, Anthropic, or other foundational model platforms.
• Familiarity with LLM techniques such as prompt engineering, RAG architecture, fine-tuning approaches (LoRA, QLoRA), and model evaluation frameworks.
• Strong logical reasoning and analytical skills to compare different AI approaches and recommend optimal solutions.
• Experience designing and running AI experiments, benchmarking models, and evaluating performance trade-offs.
• Understanding of AI system architecture, model deployment considerations, and integration patterns.
• Strong programming and experimentation skills in Python and common AI frameworks.
• Ability to clearly communicate complex AI concepts and research findings to both technical and non-technical stakeholders.
• Curiosity and passion for staying up to date with the rapidly evolving AI research and innovative landscape.

• Bachelor's degree or advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Physics, or a related quantitative discipline
• Minimum 5+ years of professional experience in AI/ML research, development, or applied machine learning roles
• Proven track record of conducting rigorous AI research, including experimentation design, benchmarking, and performance analysis
• Strong analytical and logical reasoning capabilities, with the ability to critically assess competing AI technologies and architectures
• Exceptional written and verbal communication skills, with proven ability to articulate sophisticated AI concepts to both technical and non-technical stakeholders