• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Define and evolve the technical architecture for Caseware’s AI platform, establishing architectural standards, contracts, and design principles that enable scalable, secure, reusable, and trustworthy AI capabilities across multiple teams. • Design, build, and operate AI systems powered by LLMs, retrieval pipelines, agentic workflows, and AI-enabled services, including multi-agent systems, agent execution runtimes, workflow orchestration, memory management, and human-in-the-loop AI capabilities. • Design and implement evaluation, governance, auditability, and traceability frameworks that ensure AI systems are measurable, explainable, compliant, and continuously improving. • Translate advances in AI into practical, maintainable platform capabilities that solve real customer problems. • Influence technical direction through architecture reviews, RFCs, design discussions, and hands-on collaboration. • Evaluate emerging AI technologies, models, frameworks, and cloud capabilities. • Lead proof-of-concepts that inform platform strategy and future investments. • Balance delivery speed, quality, maintainability, and long-term platform health when making technical decisions. • Drive reliability, observability, monitoring, alerting, and incident response practices across AI systems and platforms. • Ensure systems are resilient, scalable, cost-effective, and operationally efficient in production environments. • Partner closely with Product, QA, Security, Domain SMEs, and Engineering teams to deliver business value. • Act as a trusted technical leader, helping teams succeed through collaboration, influence, and shared ownership. • Mentor engineers across teams and contribute to raising overall engineering maturity.