Specialist Product Owner, Actimize ( BFSI, AI)

About this role

At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.

So, what’s the role all about?

This role offers the opportunity to shape products that combat financial crime while ensuring adherence to complex regulatory requirements and working as a Product Owner in a matrix structure. The ideal candidate will combine domain expertise, strategic thinking, technical acumen, and a practical understanding of Generative AI and Agentic AI concepts to deliver impactful solutions.

How will you make an impact?

• Product Strategy & Roadmap Development

• Define and execute the product vision, strategy, and roadmap for FCC solutions.

• Align product initiatives with organizational goals and client needs, focusing on innovation and compliance.

• Regulatory and Industry Expertise

• Stay updated on global financial crime regulations and ensure product offerings meet compliance requirements.

• Analyse emerging trends in financial crime to identify opportunities for product enhancement.

• Solution Design & Development

• Collaborate with cross-functional teams, including domain experts, engineers, data scientists, and AI specialists, to design solutions for the Financial Crime and Compliance platform, including AI-assisted and Agentic AI-enabled workflows where relevant.

• AI & Agentic Innovation

• Identify practical opportunities to apply Generative AI and Agentic AI concepts, such as AI agents, workflow automation, decision support, and assisted investigations, to improve product outcomes and user productivity.

• Translate AI/Agentic AI concepts into clear product requirements, user stories, acceptance criteria, and measurable business value while partnering with engineering and data science teams.

• Demonstrate conceptual understanding and some hands-on exposure with LLM-based solutions, prompt-driven workflows, RAG patterns, AI agents, or enterprise AI tools.

• Support responsible AI practices by considering governance, explainability, security, privacy, model limitations, and compliance expectations in product design.

• Stakeholder Collaboration

• Partner with clients, business stakeholders, and internal teams to gather insights and translate them into actionable product requirements.

• Act as the voice of the customer and ensure the delivery of solutions that exceed expectations.

• Agile Product Management

• Own the product backlog, prioritize features, and define user stories with clear acceptance criteria.

• Work closely with Scrum teams to deliver high-quality, timely product releases.

• Data-Driven Decision Making

• Leverage data and analytics to assess product performance, measure ROI, and identify areas for improvement.

• Recommend enhancements based on insights from client feedback and market analysis.

Have you got what it takes?

• Experience:

• 9-12 years of product management experience, preferably in Financial Crime, Compliance, or Financial Services domains. Experience contributing to AI-enabled products, intelligent automation initiatives, GenAI solutions, or Agentic AI use cases is highly desirable.

• Domain Expertise:

• Strong knowledge of financial crime regulations, compliance requirements, and risk management practices.

• Analytical & Problem-Solving:

• Strong problem-solving skills and ability to synthesize complex requirements into actionable product strategies.

• Proficiency in data analysis and metrics-driven decision-making.

• Technical Skills:

• Experience working with Agile methodologies and tools like Jira/Confluence.

• Understanding of APIs, data modelling, and scalable system design.

• Familiarity with Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), AI agents, and agent orchestration concepts.

• Conceptual understanding of Agentic AI patterns, including task planning, tool usage, human-in-the-loop review, and workflow automation.

• Some hands-on exposure to AI tools or prototypes, such as prompt engineering, AI-assisted workflows, RAG experiments, low-code AI agents, or collaboration with engineering teams on proof-of-concepts.

• Ability to convert AI capabilities into product requirements, customer value propositions, adoption metrics, and responsible implementation…