AI Agent Manager, Operations

Division: PortSpend Management The AI Agent Manager, Operations is responsible for identifying, designing, building, and deploying AI and automation agents across operational workflows. This is a hands on, senior individual contributor role focused on driving bottom up adoption of AI powered processes within operations teams. The role exists to reduce manual effort in document processing, data extraction, workflow routing, scheduling, and exception handling, while improving accuracy, throughput, and operational consistency. The AI Agent Manager works closely with operations leaders, process owners, and technology teams to identify high value automation opportunities, prototype solutions, measure adoption and impact, and scale what works across the operations function. Responsibilities - Use Case Discovery and Prioritisation: Identify and evaluate high value opportunities to deploy AI agents across operations, including document processing, data extraction, workflow routing, exception handling, scheduling, and compliance checks. Prioritise based on effort, impact, and adoption readiness. - Agent Design, Build, and Deployment: Design, configure, and deploy AI agents and automated workflows using LLMs, APIs, and integration tools. Own the end to end lifecycle from prototype through production deployment, including testing, monitoring, and iteration. - Operational Efficiency Improvement: Reduce manual effort in repetitive operational workflows by embedding AI agents into daily processes. Measure time saved, error reduction, and throughput improvements. Ensure agents maintain accuracy and auditability standards required by operations. - Adoption and Change Management: Drive bottom up adoption by working directly with operations teams and individual contributors. Provide hands on training, create playbooks, run workshops, and build internal champions. Track adoption metrics and iterate based on user feedback - Data Quality and Integration: Ensure AI agents operate on clean, reliable data by working with data teams and system owners. Define data requirements, build integration points with operational systems, ERP, and document management platforms, and establish feedback loops to improve agent accuracy over time. - Performance Measurement and Reporting: Define and track KPIs for each deployed agent including adoption rates, processing accuracy, throughput gains, exception rates, and ROI. Report outcomes to operations leadership and use evidence to inform scaling decisions and investment cases. - Cross Functional Collaboration: Partner with Product, Engineering, and IT to align agent deployments with platform capabilities, security requirements, and data governance standards. Ensure agents are built within approved frameworks and comply with company policies and regulatory requirements. - Experimentation and Innovation: Stay current with emerging AI capabilities, LLM developments, and automation tools relevant to operations. Run structured experiments to test new approaches and share learnings across the organisation to build collective AI fluency. - Documentation and Knowledge Management: Maintain clear documentation for all deployed agents including design rationale, configuration, dependencies, known limitation,, and escalation paths. Build a reusable library of agent patterns and templates for operational use cases. - Vendor and Tool Evaluation: Evaluate and recommend AI tools, platforms, and vendors relevant to operational use cases. Provide informed build versus buy recommendations within group guardrails. Requirements: Qualifications and Education Bachelor degree in Operations Management, Engineering, Computer Science, Data Science, or related discipline. Certifications in process improvement, AI, or automation platforms are a plus. Work Experience · 3 to 5 years experience in operations, process improvement, or operational technology roles. Demonstrated experience deploying AI tools, LLM based agents, or workflow automation in an operational environment. Experience with document processing, data extraction, workflow orchestration, and integration with operational systems. Track record of driving adoption of new tools and processes across operations teams. Experience in maritime, logistics, or B2B services preferred. Skills and Knowledge · Strong understanding of LLMs, prompt engineering, and AI agent frameworks. Practical experience with automation platforms, document processing tools, and API integrations. Deep knowledge of operational workflows including document handling, data validation, exception management, and process orchestration. Data analysis and KPI design skills. Change management ability to drive adoption bottom up without formal authority. Strong communication skills to translate technical concepts for operational audiences. Claude Enterprise and Cowork Skills · Proficiency in Anthropic’s Claude platform including Claude for Enterprise and Claude Teams administrati

Use Case Discovery and Prioritisation: Identify and evaluate high value opportunities to deploy AI agents across operations, including document processing, data extraction, workflow routing, exception handling, scheduling, and compliance checks. Prioritise based on effort, impact, and adoption readiness.\nAgent Design, Build, and Deployment: Design, configure, and deploy AI agents and automated workflows using LLMs, APIs, and integration tools. Own the end to end lifecycle from prototype through production deployment, including testing, monitoring, and iteration.\nOperational Efficiency Improvement: Reduce manual effort in repetitive operational workflows by embedding AI agents into daily processes. Measure time saved, error reduction, and throughput improvements. Ensure agents maintain accuracy and auditability standards required by operations.\nAdoption and Change Management: Drive bottom up adoption by working directly with operations teams and individual contributors. Provide hands on training, create playbooks, run workshops, and build internal champions. Track adoption metrics and iterate based on user feedback\nData Quality and Integration: Ensure AI agents operate on clean, reliable data by working with data teams and system owners. Define data requirements, build integration points with operational systems, ERP, and document management platforms, and establish feedback loops to improve agent accuracy over time.\nPerformance Measurement and Reporting: Define and track KPIs for each deployed agent including adoption rates, processing accuracy, throughput gains, exception rates, and ROI. Report outcomes to operations leadership and use evidence to inform scaling decisions and investment cases.\nCross Functional Collaboration: Partner with Product, Engineering, and IT to align agent deployments with platform capabilities, security requirements, and data governance standards. Ensure agents are built within approved frameworks and comply with company policies and regulatory requirements.\nExperimentation and Innovation: Stay current with emerging AI capabilities, LLM developments, and automation tools relevant to operations. Run structured experiments to test new approaches and share learnings across the organisation to build collective AI fluency.\nDocumentation and Knowledge Management: Maintain clear documentation for all deployed agents including design rationale, configuration, dependencies, known limitation,, and escalation paths. Build a reusable library of agent patterns and templates for operational use cases.\nVendor and Tool Evaluation: Evaluate and recommend AI tools, platforms, and vendors relevant to operational use cases. Provide informed build versus buy recommendations within group guardrails.