AI Engineer – Design & Deploy AI & RPA Solutions

About the role

ZB Financial Holdings is seeking experienced AI Engineers to design, develop, and deploy AI, machine learning and RPA solutions that improve operational efficiency and support data‑driven decision making across its financial services portfolio.

Key responsibilities

• Design, develop and launch AI, machine learning and generative AI solutions that meet business needs, disaster‑recovery and continuity requirements.
• Build production‑grade applications using large language model integration, retrieval‑augmented generation, agentic workflows, prompt/context engineering and justified fine‑tuning.
• Identify, prioritize and translate automation opportunities into a delivery roadmap.
• Analyze business processes and implement AI/RPA solutions to boost accuracy, speed, cost efficiency and customer experience.
• Integrate AI and automation into core banking, insurance and enterprise systems while preserving reliability.
• Establish pipelines, monitoring, evaluation and retraining processes to keep models performant and cost‑effective.
• Define rigorous testing strategies covering accuracy, latency, cost, robustness and security.
• Mitigate AI‑specific risks such as model bias, hallucination, data leakage and prompt injection.
• Ensure compliance with regulatory, data‑protection and security standards.
• Evaluate and recommend AI tools, models, frameworks and platforms, managing vendors within delegated authority.
• Document models, workflows and integrations for auditability and supportability.
• Provide technical leadership, mentorship and coaching to junior engineers.

Required profile

• Bachelor’s degree in Computer Science, Information Systems, Artificial Intelligence, Software Engineering or equivalent professional experience.
• Proven experience designing and deploying AI/ML and RPA solutions in a regulated financial services environment.
• Strong understanding of enterprise architecture, security and compliance requirements.

Required skills

• Large language model integration
• Retrieval‑augmented generation
• Prompt and context engineering
• Model fine‑tuning
• Robotic Process Automation (RPA)
• AI/ML model development and monitoring
• Testing and evaluation of AI systems
• Risk mitigation for model bias, hallucination, data leakage and prompt injection