Lead Process engineer - AI Transformation

You will be the backbone of the programme's delivery engine - responsible for consolidating, tracking, and governing the transformation roadmap across all securities workstreams. Beyond governance, you will be hands-on: designing and writing reusable AI skills, contributing to deep agent design, preparing data and ground truth, coordinating testing, and driving stakeholder alignment.

This is not a passive PMO role. You will actively shape what gets built, how it gets sequenced, and how it scales - while also ensuring the analytical groundwork (data, documentation, validation) is in place to keep the AI delivery pipeline moving.

Roadmap & Delivery Management

Own the consolidated transformation roadmap across all business lines and workstreams, maintaining a single source of truth for delivery status

Track milestones, flag risks, and manage dependencies across parallel delivery tracks

Ensure delivery sequencing respects architectural constraints and platform readiness

Drive execution discipline - making sure that what is planned gets delivered, and what is at risk gets escalated early

Cross-Functional Coordination

Manage interdependencies between AI delivery teams, IT infrastructure, platform engineering, and operations

Coordinate API readiness, model deployment cycles, and UI integration timelines across teams

Ensure parallel workstreams (e.g., prompt development, template changes, deployment gates) stay synchronised

Governance & Stakeholder Communication

Prepare and present programme status to steering committees and senior leadership

Run governance forums that keep business, IT, and AI teams aligned on priorities and trade-offs

Produce clear, technically informed executive materials that translate complexity into actionable insight

Map and engage all key stakeholders; maintain a validated view of processes, data flows, and dependencies

Skills Design & Development

Design, write, and maintain reusable AI skills - structured, modular prompt-based capabilities that standardise how agents perform classification, extraction, and decision-making across use cases

Translate business processes and operational logic into well-structured skill files that can be executed by the agentic platform

Ensure skills are documented, version-controlled, and validated against production accuracy standards before deployment

Collaborate with platform engineering to ensure the technical pipeline supports skill ingestion, testing, and execution at scale

Build a reusable skills library that can be adopted across multiple business cases and asset classes

Deep Agent & End-to-End Orchestration

Contribute to the design and delivery of deep agents - multi-step, multi-agent workflows that orchestrate classification, extraction, validation, and downstream action in a single end-to-end flow

Define orchestration logic, guardrail rules, and memory strategies that enable agents to handle complex, multi-turn decision flows autonomously

Work with AI engineers to validate that structural sub-agents (orchestrator, controller, memory, guardrail) behave correctly across real-world scenarios

Support the establishment of deep agent patterns as reference models for future AI-led process transformations

Data, Testing & Analytical Support

Collect, curate, and annotate email samples and operational data to build ground truth datasets for securities use cases

Support current-state and target-state process mapping using VSM and CTA methodologies; document workflows, decision logic, and exception handling procedures

Learn and understand the existing library of AI skills and prompts to identify reuse opportunities across new use cases

Coordinate UAT cycles, design test scenarios, manage test execution, and track defects to resolution

Gather business requirements from operations teams and serve as the bridge between business users and AI delivery squads

Methodology & Reusable Assets

Document transformation patterns and frameworks that can be replicated across business units

Contribute to the programme's knowledge base with playbooks, case studies, and lessons learned

Support the identification and enablement of AI Champions within operations teams

5 years in programme management, transformation, or change delivery within investment banking or financial services

Strong understanding of post-trade securities operations - prematching, settlement, SDI, or similar back-office domains

Track record of managing complex, multi-stream delivery roadmaps with interdependent workstreams

Experience producing executive-level governance materials and running steering committees

Ability to translate business processes into structured AI instructions (skills/prompts) - strong written communication and logical structuring skills are essential

Exposure to AI, automation, or digital transformation programmes (understanding of agentic AI, prompt engineering, or NLP is a strong plus)

Strong data skills - comfortable working with datasets, annotations, and structured data formats

Experience coordinating UAT or testing cycles in a technology delivery environment

Comfortable operating in a fast-paced environment where priorities evolve and ambiguity is normal

Experience with multi-agent AI systems, LLM-based automation, or intelligent document processing

Hands-on experience writing prompts, skills, or structured instructions for AI/LLM systems

Familiarity with Value Stream Mapping (VSM) or Cognitive Task Analysis (CTA)

Background in building reusable frameworks or transformation toolkits at scale