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 prioritise 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 realised and reported quarterly (hours returned, annualised 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/summarisation, 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, prioritisation 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 containerisation 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
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