AI Transformation Lead

Description One Zero Digital Bank is looking for a hands-on AI Solutions Engineer to accelerate practical, governed AI adoption across the bank’s business functions. This role is for a builder with strong product instincts: someone who can sit with Operations, Legal, GRC, Compliance, Finance, Risk, Advisory, and other non-R&D teams, understand how work actually happens, identify high-value manual workflows, and turn them into secure AI-powered solutions. You will design, build, pilot, and scale internal AI tools, agent-powered workflows, copilots, automations, and business-facing AI applications using platforms such as AWS Bedrock, Google Vertex AI, Gemini Enterprise, Azure OpenAI, Claude Code, workflow automation tools, and similar technologies. This is not a strategy-only role. It is also not a pure engineering role. It is a hands-on, forward-deployed builder role at the intersection of Product, Engineering, Operations, and Enterprise AI Transformation. Your Day-to-Day Partner directly with non-R&D teams to understand workflows, pain points, decision processes, manual tasks, and operational bottlenecks. Identify and prioritize AI and automation opportunities based on business impact, feasibility, adoption potential, data readiness, cost, security, risk, compliance, and control requirements. Translate ambiguous business problems into clear AI use cases, MVP definitions, solution designs, success metrics, and rollout plans. Build and configure AI-powered solutions, including internal copilots, agentic workflows, workflow automations, RAG-based tools, document-processing flows, decision-support tools, and integrations with internal systems. Prototype quickly, validate with users, and iterate based on real workflow feedback. Work with Engineering, Data, Security, Legal, Risk, and Compliance teams to ensure solutions are safe, governed, auditable, and aligned with bank standards. Lead pilots and production rollouts, including training, enablement, documentation, and adoption support. Track usage, reliability, quality, cost, productivity gains, and business outcomes after launch. Requirements 5+ years in a technical role such as software engineering, solutions engineering, technical product management, automation engineering, data/AI engineering, or a similar hands-on role. 1+ years of practical experience delivering AI, GenAI, agentic, or automation solutions for business or operational users. A clear builder track record: you have shipped tools, automations, workflows, internal products, or prototypes that people actually used. Hands-on experience with AI platforms, coding agents, LLM APIs, agent frameworks, workflow automation tools, low-code/no-code platforms, or enterprise AI products. Practical understanding of LLMs, RAG, prompt design, AI agents, tool use, human-in-the-loop workflows, evaluations, and responsible AI patterns. Product judgment: ability to prioritize, define MVPs, separate nice-to-have ideas from high-value use cases, and measure outcomes. Strong discovery skills with non-technical stakeholders: you can map how work happens today and redesign it around AI, automation, and human accountability. Excellent communication skills: able to explain technical tradeoffs to business stakeholders and business context to technical teams. Strong ownership and execution: comfortable moving from ambiguous problem to working solution. Experience in a regulated environment such as banking, fintech, insurance, payments, healthcare, or another high-control industry is a strong advantage.