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, and 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 such as 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 limitations 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.
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 bottom‑up adoption without formal authority.
Strong communication skills to translate technical concepts for operational audiences.
Proficiency in Anthropic's Claude platform, including Claude for Enterprise and Claude Teams administration, user provisioning, workspace configuration and usage policy management.
Hands‑on experience with Claude COWork mode for desktop automation, skill authoring, custom plugin development and deployment, scheduled‑task configuration using cron, Claude Code CLI, hooks, slash commands, MCP, server integration and multi‑step agent workflows.
Advanced prompt engineering for Claude models, including system prompts, structured output formatting, tool definitions, chain‑of‑thought techniques and context‑window optimisation for enterprise‑scale document processing.
Knowledge of Anthropic API or OpenAI, Claude Agent SDK, function calling, streaming responses, batch processing and model selection across Claude Opus, Sonnet and Haiku variants for cost and latency optimisation.
Object‑oriented…
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