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Leads payments consulting and transformation engagements for financial institutions across retail banking and domestic and cross-border payment ecosystems. Responsibilities include facilitating discovery workshops, gathering requirements, designing target-state architectures and operating models, developing delivery roadmaps, advising senior stakeholders, producing executive deliverables, leading multidisciplinary teams, managing client relationships, and supporting business development.

Lead consulting engagements that define enterprise data strategies, design scalable cloud-based data platforms, and drive data governance, cataloguing, privacy, metadata, migration, and security capabilities. Advise financial services clients, communicate technical concepts to diverse stakeholders, lead teams, and contribute to Capco’s data architecture offerings.

Build and deploy production-grade AI solutions for financial services, including multi-agent workflows, LLM and neural network applications, RAG systems, data pipelines, and automation tools. Collaborate with technical and business stakeholders, fine-tune models, monitor production systems, and ensure regulatory, privacy, ethical AI, and scalability requirements are met. Participate in code reviews, agile ceremonies, and CI/CD processes.

Support banking AI transformation by managing parallel use-case analysis workstreams, facilitating stakeholder workshops, gathering evidence, documenting processes and requirements, and scoring opportunities. Produce decision-ready dossiers covering business value, feasibility, risk, controls, regulatory obligations, and implementation complexity. Assess responsible AI requirements including the EU AI Act, privacy, cybersecurity, explainability, auditability, human oversight, and reproducibility while maintaining evidence quality and human review.

Design, develop, integrate, test, deploy, and monitor secure AI solutions for banking use cases. Translate business requirements into technical architectures and production capabilities, including Generative AI and Agentic AI applications. Implement responsible AI controls such as access management, logging, explainability, auditability, privacy, human oversight, and reproducibility. Collaborate with business, product, data, risk, technology, and client stakeholders in regulated environments.

Leads strategic AI transformation and delivery engagements for banking clients. Owns project governance, delivery roadmaps, stakeholder decision-making, AI use-case prioritization, operating model design, risk management, and senior-management communications. Ensures responsible AI implementation through human oversight, guardrails, review, auditability, and escalation pathways.

Support strategic advisory engagements focused on retail banking payments. Conduct market, competitive, regulatory, and ecosystem research; structure ambiguous business questions; synthesize quantitative and qualitative findings; evaluate strategic options; support stakeholder interviews and workshops; and create executive-level presentations with evidence-based recommendations for senior clients.

Conduct security assessments, identify vulnerabilities and risks, advise clients on cybersecurity frameworks and compliance, and design or implement tailored security solutions. Responsibilities include configuring SIEM, firewalls, IDS/IPS, and encryption tools; developing incident response plans, policies, procedures, and training; supporting cloud security; monitoring and reporting; and addressing emerging threats. The consultant works directly with client teams and communicates complex security concepts to nontechnical stakeholders.

Leads junior developers and oversees technical delivery, vendor engagement, technical risk management, development quality, code reviews, recruitment, onboarding, and training. Participates in the full software development lifecycle, translating business requirements into Java web and API solutions. Reviews technical specifications, mentors team members, and applies design patterns, SQL, Spring technologies, containerization, and CI/CD practices in banking or wealth-management projects.

Lead hands-on optimization of fraud machine learning models, ML pipelines, and Google Cloud infrastructure. Improve performance, reliability, resilience, and operating costs across compute, storage, networking, training, inference, and distributed data workloads. Provide implementation-ready code and configuration guidance, estimate savings and effort, and apply software engineering practices including Python, SQL, CI/CD, testing, and code review. Collaborate with infrastructure, data science, and business stakeholders while using AI tools responsibly in regulated environments.