AI Engineering Lead

Key Responsibilities: 1. Technical Architecture and Solution Design - Define the technical architecture for AI automation, workflow automation, data integration and platform capabilities across ETP. - Determine how the data lake, AI inference layer, vector database, workflow automation tools, SaaS platforms and ETP Hub should connect. - Translate business requirements and operational pain points into technical solution designs, delivery plans and prioritised build tasks. - Ensure technical solutions are secure, maintainable, reusable and aligned with ETP’s longer-term digital operating model. 2. AI Engineering Delivery Leadership - Lead the AI Engineers in delivering AI agents, automation workflows, data pipelines, reporting automation and integration components. - Review technical outputs including scripts, workflows, data logic, API integrations, AI-assisted tools and deployment approaches. - Set delivery standards for code quality, testing, documentation, release readiness and post-launch sustainment. - Support the use of AI coding tools to accelerate development while ensuring appropriate technical review and quality control. 3. Data, AI and Workflow Automation - Provide technical oversight for data cleanup, data transformation, reconciliation logic, reporting automation and AI-enabled reporting. - Guide the development of AI inference-layer capabilities for extraction, classification, validation, summarisation and report generation. - Lead workflow automation approaches using tools such as n8n and related integration platforms. - Guide AI-enabled features within ETP Hub, including search, knowledge retrieval, agent workflows and user-facing automation. 4. Platform, SaaS and Infrastructure Integration - Ensure automation solutions integrate properly with ETP’s platforms, SaaS applications, workflow tools, data sources and infrastructure. - Work with platform, SaaS and infrastructure workstreams to align technical dependencies, implementation sequencing and support arrangements. - Assess vendor and professional services proposals for technical soundness, scalability, maintainability and handover readiness. - Surface risks, constraints and trade-offs early, including security, data access, infrastructure, integration and sustainment considerations. 5. Governance, Documentation and Sustainment - Establish technical standards for automation workflows, data pipelines, scripts, AI agents, prompts, integrations and reusable components. - Ensure documentation of architecture, data flows, business rules, workflow logic, prompts, APIs, dependencies and support arrangements. - Put in place testing, monitoring and issue-resolution processes for deployed automation and AI-enabled tools. - Drive continuous improvement based on user feedback, adoption, operational performance and changing business needs. Qualifications: - Degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering or a related technical discipline. - Typically 8 or more years of relevant experience in software engineering, AI engineering, data engineering, platform integration or digital solution delivery. - At least 3 years of experience leading technical delivery, solution architecture, engineering teams or cross-functional automation workstreams. - Strong hands-on understanding of Python or similar languages, APIs, data pipelines, system integration and deployment practices. - Practical experience with AI agents, LLM workflows, RAG, embeddings, vector databases, knowledge retrieval or AI inference-layer design. - Experience with workflow automation tools such as n8n, Airflow, Zapier, Make or similar platforms. - Ability to guide engineers, review technical work, challenge vendors, make architecture decisions and translate business problems into scalable technical solutions. - Strong communication and stakeholder management skills.