Job Description

Who We Are Looking For
We are hiring a Director, AI & Automation - Shared Services to build and scale AI-enabled efficiency across Finance, HR, Legal, and Marketing. This is a player-coach role: you will be expected to personally design and deliver automation and AI solutions, while also owning the operating model (intake, prioritization, governance, and benefits tracking).
In the first 6-12 months, you should expect to spend a significant portion of your time building (coding, configuring automations, integrating systems, and hardening solutions for production). You will have the opportunity to recruit an AI Automation Engineer after you start; however, this role requires hands-on technical depth from day one.
The ideal candidate will exhibit the ability to maintain a high-level of confidentiality, organisation, attention to detail, initiative, autonomy, and judgment, as well as an ability to effectively prioritize workflow and execute assignments. This is a dynamic role that requires responsiveness and comfort working in a fast-paced environment.
• In this role, success is measured by outcomes and adoption, not experimentation for its own sake: 15-30% cycle-time reduction on targeted processes, with manual touches and exception rates reduced
• High adoption and sustainable change (target: >80% adoption in pilot groups where appropriate)
• Audit-ready controls embedded (e.g., access control, audit logging, evidence trails, and documented SOPs)
• Benefits realized and reported quarterly (hours returned, annualized net benefits, quality, and risk reductions)
• Dual OKRs: business outcomes (cycle time, hours returned, adoption, audit findings) and platform outcomes (reliability/SLA, cost per run, zero privacy incidents) in partnership with IT Solutions
This role can be based at our offices near London Bridge or Bournemouth, with 2-3 days in the office
What You Will Work On
Hands-on build and delivery (primary)
• Identify, prototype, and ship AI and automation solutions from discovery through MVP and scale (LLM workflows, RPA, workflow automation, integrations, and lightweight internal apps)
• Write production-quality code where needed (primarily Python) and build reliable integrations (APIs, webhooks, ETL-style data movement, document pipelines)
• Design and implement LLM-powered features such as document intake/summarization, classification and routing, knowledge retrieval, exception handling, and agent-assisted operations - with clear quality gates and evaluation
• Build automation that fits real operating constraints: retries, idempotency, monitoring/alerts, and clear ownership for ongoing support
• Create and maintain reusable building blocks (templates, libraries, prompt/eval harnesses, connectors) to accelerate delivery across use cases
Portfolio, ROI, and governance (shared services leadership)
• Own the use-case intake process, prioritization rubric, and shared backlog across Finance, HR, Legal, and Marketing
• Run a joint weekly backlog review with IT Solutions to align on capacity, dependencies, and delivery sequencing
• Baseline current-state metrics (cycle time, effort, error rates) and build business cases with clear assumptions and benefits tracking
• Provide monthly steering updates (progress, risks, decisions needed) and quarterly benefits reporting
• Coordinate UAT and rollout with process owners; ensure SOPs, training materials, and controls documentation are updated for sustainable adoption
Controls-first delivery with IT Solutions
• Partner with IT Solutions to design solutions that meet security and platform guardrails (SDLC, identity/RBAC, secrets management, data protection, and change control)
• Implement definition-of-done gates for AI automation releases: threat model, RBAC, audit logging, evaluation/red-team results as applicable, rollback plan, runbooks, and observability
• Coordinate with IT change control processes (e.g., CAB) where required, and ensure releases are supportable with clear ownership and monitoring
• Ensure vendor/tooling choices and data flows are compliant with relevant policies and regulations (e.g., SOX controls where applicable, GDPR/data privacy requirements)
Team building and capability development
• Recruit, mentor, and lead additional capacity (e.g., an AI Automation Engineer) as the portfolio scales; set standards for code quality and delivery hygiene
• Build strong working relationships across Shared Services and IT, creating clear handoffs and support models (who owns what post-launch)
• Drive practical enablement for teams: patterns, documentation, and best practices to increase automation literacy and adoption
Example focus areas
• Finance: AP/AR workflows, month-end close support, reconciliations, invoice and payment exception handling
• HR: case intake and routing, employee request workflows, knowledge retrieval for standard policy questions
• Legal: contract intake, clause classification, triage and workflow routing, obligation tracking support
• Marketing: content operations support, approvals workflows, internal reporting and request handling
30-60-90 Day Plan
First 30 days
• Align on an operating agreement (capacity, intake/backlog, decision rights, and definition-of-done / release gates)
• Baseline metrics for priority processes; stand up repos, logging, and an evaluation harness for AI features
• Deliver a short list of MVP candidates with business cases and delivery plans
By 60 days
• Ship 2-3 MVPs through the full security/evaluation pipeline and into pilot groups
• Publish the first KPI dashboard for adoption and benefits tracking
• Establish a repeatable delivery pattern (templates, runbooks, monitoring, and support model)
By 90 days
• Scale at least one MVP (broader rollout, hardened operations, and measurable benefits)
• Tune reliability and delivery throughput (SLOs, definition-of-done, backlog capacity planning)
• Finalize hiring plan and begin recruiting additional engineering capacity as needed
Who You Will Work With
• SVP Finance (initial direct manager) to set priorities, remove roadblocks, and track benefits like a P&L
• Process owners across Finance, HR, Legal, and Marketing to define requirements, run UAT, and drive adoption
• IT Solutions to align on architecture, integrations, security controls, platform guardrails, and supportability
• Optimization / Business Systems partners to coordinate process redesign and change management where needed
What You Will Bring
• Experience leading platform decisions across automation ecosystems and deploying scalable solutions
• Proven experience delivering automation and AI solutions in an enterprise environment (MVP through production)
• Strong hands-on engineering skills, with high proficiency in Python for APIs, integrations, and AI-driven workflows
• Experience with enterprise LLM platforms (e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic Claude)
• Experience implementing LLM-based solutions (prompting, retrieval-augmented generation, tool/function calling, document processing, and evaluation/quality testing)
• Experience with workflow automation and/or RPA platforms (e.g., UiPath, Microsoft Power Automate, Automation Anywhere, ServiceNow workflows) or equivalent automation experience
• Solid software engineering fundamentals: version control, testing, code review, packaging, CI/CD concepts, and documentation
• Familiarity with modern data platforms and pipelines (e.g., Databricks, Snowflake, Palantir Foundry, Airflow, Kafka)
• Experience deploying solutions on cloud platforms (Azure, AWS, or GCP) with containerization and CI/CD tooling
• Comfort partnering with security and IT to design controls: identity/RBAC, secrets management, audit logs, and change control
• Ability to translate ambiguous business problems into shipped solutions; strong stakeholder communication skills
• Experience working cross-functionally with business teams and driving adoption (training, rollout planning, and SOP updates)
Preferred
• Experience in Shared Services, Global Business Services, finance operations, or other back-office process transformation roles
• Working knowledge of Go and/or TypeScript for building services or lightweight internal tools; willingness to learn new stacks as needed
• Experience building monitoring/observability (metrics, logs, tracing) and operating production services
• Exposure to risk, compliance, audit, or regulated environments; ability to design evidence trails and control documentation