• 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.

• Design, build, and maintain LLM-powered solutions including custom GPTs and domain-specific assistants. • Develop and optimize prompts, system instructions, and context strategies for accuracy and reliability. • Implement prompt chaining, tool calling, and basic agentic workflows. • Build and support Retrieval-Augmented Generation (RAG) pipelines using vector databases. • Evaluate, test, and improve LLM outputs using defined quality and safety criteria. • Apply responsible AI principles, safety guardrails, and governance standards in all implementations. • Develop full stack AI-enabled applications including backend services, APIs, and UI integrations. • Integrate LLM capabilities into existing enterprise applications. • Build reusable services and components to support AI features. • Debug and resolve application or AI-related issues impacting functionality or user experience. • Develop and deploy AI solutions on Azure and/or AWS. • Support CI/CD pipelines for application and AI workloads. • Assist with MLOps workflows including model versioning, deployment, and monitoring. • Ensure solutions are secure, reliable, and cost-aware in production environments.