Senior Consultant Specialist

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We are currently seeking an experienced professional to join our team in the role of Senior Consultant Specialist.

Business: CCM and Fin Resource Mgt Tech

Job ID：51290

Principal responsibilities

Portfolio & Programme Management (Strategic Delivery)

• Accountable for end-to-end delivery of strategic portfolio outcomes that support business vision and purpose, delivered through complex, global change programmes.
• Own and run portfolio governance across the Value Stream (VS) and Sub-Value Streams (SVS), partnering closely with Business Transformation Portfolio teams to ensure joined-up oversight and decision-making.
• Establish and maintain effective governance and oversight, including portfolio forums, working groups, clear escalation routes, and timely executive briefings.
• Proactively identify early warning indicators of delivery risk (schedule, scope, cost, resourcing, dependency health) and drive appropriate resolution paths with Tech Leads and stakeholders.
• Provide clear, concise portfolio status updates to central portfolio teams to enable consolidated reporting and escalation of items requiring senior management attention.
• Embed lessons learned and continuous improvement loops across the VS/SVS to strengthen delivery maturity over time.

Governance, Reporting Standards & Quality Assurance

• Apply agreed reporting standards and provide quality assurance across SVS for RAIDs, governance packs, reporting cadence, change framework adherence, and tooling usage.
• Ensure Technology RAIDs are accurately maintained in the System of Record (Clarity), including Level 1–5 risks, issues, and dependencies, with strong auditability and traceability.
• Own oversight and key assurance controls, conducting periodic data quality checks across VS/SVS to improve accuracy, completeness, and consistency of portfolio data.
• Support Change Framework and reporting by agreeing and embedding consistent standards across VS/SVS, improving comparability and decision effectiveness.
• Quality assure technical delivery tracking through application of bank change execution controls, leveraging data and automation to reduce manual effort and increase reliability.
• Drive thematic analysis across portfolio risks and issues to produce executive-level insights and corrective action summaries (root causes, trends, hotspots, and required interventions).

Portfolio Forums & Executive Communication

• Support and run Technology VS governance forums: build decks, produce status reporting, manage actions/decisions, and facilitate monthly governance meetings.
• Ensure CCM Tech Head and SVS Tech Leads are fully briefed on programme status, key risks/issues, dependencies, and required decisions—no surprises, just options and trade-offs.

Business Cases & Planning (Funding, Prioritisation, Resourcing)

• Partner with Business Transformation teams to standardise planning processes so technology deliverables are clearly defined, funded, resourced, prioritised, and sequenced.
• Oversee technology inputs to business cases across the VS, ensuring completeness, quality, and adherence to Group standards through to submission and approval.

Leadership & Collaboration

• Build and sustain long-term relationships with critical stakeholders across Technology, Business Transformation, and central portfolio functions, enabling fast alignment and effective delivery.
• Act as an active member of the CCM Tech leadership team, contributing to portfolio direction, delivery discipline, and continuous improvement.
• Operate as a “critical friend” to Value Streams—supportive but appropriately challenging—strengthening delivery outcomes through constructive governance, transparency, and shared accountability.
• Collaborate with central portfolio teams to adopt best practices, improve tooling, and uplift portfolio management capability across the organisation.

Knowledge & Experience/Qualifications

• Design, prototype, and build applied AI solutions using LLMs, RAG, agentic workflows, and AI service patterns.
• Develop AI-enabled applications, APIs, and reusable components for internal tools, product features, and enterprise use cases.
• Build and improve retrieval workflows, including document processing, chunking, embeddings, search, ranking, grounding, and answer quality controls.
• Implement agentic workflows and multi-agent patterns for task automation, reasoning loops, tool use, and workflow orchestration.
• Apply prompt engineering, context engineering, and loop/workflow engineering to improve AI system reliability and usefulness.
• Use at least one agent framework or library, such as LangChain, Azure Agent Framework, Google ADK, or equivalent tools.
• Design and run evaluations for agents, prompts, RAG pipelines, and AI application behavior, including quality, reliability, and regression checks.
• Integrate AI services with APIs, data sources, internal systems, MCP servers, tools, and skills-based workflows where applicable.
• Build AI services with production-minded practices, including code quality, testing, configuration, deployment readiness, and basic observability.
• Stay curious about emerging AI engineering practices and assess how new approaches can improve delivery speed, quality, and user outcomes.

What additional skills will be good to have?

• Significant hands-on experience in AI engineering, applied AI, software engineering, or related technical roles.
• Strong Python development skills and experience building production-quality services, APIs, or automation workflows.
• Practical experience building RAG solutions or retrieval-based AI applications.
• Experience with LLM application patterns, prompt engineering, context management, tool use, and AI workflow orchestration.
• Experience with at least one agent framework or library, such as LangChain, Azure Agent Framework, Google ADK, or similar.
• Understanding of agentic workflows, multi-agent systems, MCP, skills, and tool-based AI application design.
• Experience defining or running AI evaluations for prompts, agents, RAG pipelines, or model-assisted workflows.
• Working knowledge of ML concepts, experimentation, model evaluation, data pipelines, and AI/ML lifecycle considerations.
• Light deployment knowledge, including APIs, containers, environment configuration, CI/CD basics, and collaboration with DevOps or platform teams.
• Strong problem-solving, documentation, communication, and cross-functional collaboration skills
• Experience with OpenTelemetry or observability practices for AI services and distributed applications.
• Experience with Docker or containerized application development.
• Exposure to cloud AI services, vector databases, search platforms, workflow orchestration tools, or enterprise integration platforms.
• Experience building AI tools for knowledge management, document automation, workflow acceleration, decision support, or business process improvement.
• Curiosity and practical awareness of cutting-edge AI practices, including agent design patterns, evaluation methods, MCP ecosystems, and AI coding/productivity workflows.