AI Engineer

• 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.

Senior AI Software Engineer

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up.
• Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration.
• Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams.
• Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value.
• Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces.
• Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions.
• Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms.
• Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption.
• Participate in production support and incident response for owned systems, helping define and uphold reliability and operational best practices.
• Mentor AI team members through code reviews, design feedback, and hands-on guidance, raising the overall quality bar of the team.

AI Engineer II

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products.
• Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively.
• Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment.
• Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices.
• Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications.
• Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows.
• Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators.
• Write production-grade Python code to support models, pipelines, and experimental frameworks.
• Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

Senior AI/ML Engineer – Technology

Machine Learning Engineer

• Design, build, and maintain scalable machine learning models that improve and automate logistics processes for our customers.
• Own projects end-to-end, from problem definition and data exploration to model deployment and monitoring in production.
• Collaborate closely with engineering teams to align ML work with customer needs and deliver features that drive business value.
• Serve as a technical leader and mentor within the ML area, reviewing code and ensuring best practices for reproducibility, quality, and performance.

• Diseñar, desarrollar e implementar soluciones de IA Generativa.
• Construir soluciones con Python, LLMs, RAG y agentes de IA.
• Integrar IA con APIs, bases de datos, plataformas legacy y sistemas empresariales.
• Trabajar sobre ambientes AWS.
• Colaborar con equipos técnicos y stakeholders en inglés.
• Desarrollar soluciones escalables, seguras y orientadas a producción.

• Design, build, and operate AI agents that perform multi-step reasoning, decision-making, and task execution across enterprise systems.
• Develop agent-based solutions using LLMs, tools/function calling, memory, and orchestration frameworks to automate end-to-end business processes.
• Implement human-in-the-loop, approval, audit, and exception-handling patterns to ensure safe and reliable automation.
• Define and track KPIs for agent effectiveness (e.g., cycle-time reduction, automation rate, error rate, cost savings) and continuously optimize performance.
• Design and maintain AI-driven workflows that integrate with enterprise applications (CRM, ERP, ticketing, content management, collaboration platforms).
• Build and expose services and APIs that allow internal applications to invoke AI agents and automation capabilities.
• Integrate and govern third-party AI platforms and services where they accelerate delivery and reduce operational risk.
• Ensure reliability, observability, and cost controls across automated processes.
• Develop and deploy LLM-powered capabilities including tool-using agents, prompt strategies, embeddings, vector search, and retrieval-augmented generation (RAG) where appropriate.
• Evaluate model and agent behavior using quantitative metrics, structured test cases, and regression suites.
• Apply guardrails, validation, and monitoring to ensure responsible, predictable, and secure AI behavior in production.
• Operate AI agents and automation solutions in production, responding to incidents, failures, and unexpected behaviors.
• Partner with IT Operations and Security to ensure solutions meet availability, security, privacy, and compliance requirements.
• Participate in on-call or escalation rotations as required for critical AI-enabled systems.
• Partner with business stakeholders to identify high-impact automation opportunities and translate them into AI agent designs.
• Provide technical leadership, design guidance, and mentorship to engineers working on AI and automation initiatives.
• Contribute to architectural standards, best practices, and documentation for agent-based AI systems.

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