AI & Automation Engineer

VFX Financial

5d•Hybrid

About The Position

VFX Financial is a regulated fintech providing payments, foreign exchange, treasury services, and multi-currency accounts. We have deployed enterprise LLM tooling organisation-wide (Claude Enterprise) across all teams, but lack dedicated resource to drive adoption, govern use, and deliver production automations. This hire owns the AI adoption and automation delivery mandate. This is primarily an AI adoption and automation delivery role—not a data engineering role. The engineer will work closely with Operations, Finance, Compliance, and Technology teams to identify automation opportunities, build production-grade solutions, and ensure every deployment is safe, governed, and measurable. Data pipeline engineering and Snowflake modelling sit with the data engineering team; this role uses the data platform as a consumer and integration point. The tech stack includes Azure, Power Automate, Python, the Claude API, and Microsoft 365 integrations. The engineer operates within the AI governance framework owned by Compliance, Risk, and the DPO, contributing technical evidence and controls, not setting policy. This role can be hybrid at our office in Portimao or fully remote across Portugal.

Requirements

3–6 years of hands-on software or automation engineering experience
Proficiency in Python (e.g. scripting, API integration, workflow automation)
Practical experience building with enterprise LLM APIs, such as Claude API, OpenAI, Azure OpenAI, or equivalent
Experience with Power Automate or equivalent low-code/no-code automation platforms in a production context
Ability to gather requirements from non-technical stakeholders and translate into working solutions
Understanding of production controls: secrets management, versioning, observability, error handling, and rollback
Strong written English (e.g. documentation, runbooks, and stakeholder communication)

Nice To Haves

Experience with agentic AI patterns and multi-step LLM workflows with human oversight
Background in financial services, payments, or another regulated sector
Familiarity with Azure services used as integration and compute infrastructure
Experience with the Microsoft 365 ecosystem as an integration target (Graph API, Teams, SharePoint)
Awareness of AI governance frameworks and practical privacy controls for LLM deployments

Responsibilities

Work with business stakeholders across Operations, Finance, and Technology to identify, prioritise, and scope AI and automation use cases
Maintain and manage the AI use-case register, including intake, prioritisation, status tracking, and outcomes measurement
Drive adoption of deployed AI tools (Claude Enterprise and equivalents) — structured rollout, training, and usage measurement by function
Stay current on enterprise LLM and AI tooling developments; advise the Data & AI Lead on relevant capabilities
Design and build production-grade automations using Power Automate, Python, and API integrations, targeting operational workflows across payments, FX, treasury, onboarding, and internal operations
Build and maintain automated workflows and AI pipelines with human-in-the-loop controls, escalation paths, and audit trails
Integrate LLM workflows with internal systems and third-party APIs using the Claude API or equivalent enterprise LLM APIs
Document all automations with clear operational runbooks; ensure solutions are maintainable by others
Apply production engineering standards to all AI deployments: secrets management, prompt and model versioning, evaluation datasets, output quality thresholds, and observability
Implement cost monitoring and usage controls for LLM API consumption
Build and maintain rollback and kill-switch mechanisms for all production AI workflows
Apply personal data minimisation principles — no personal data in prompts unless explicitly reviewed and approved
Produce the technical input for vendor, security, privacy, and data-residency assessments for any new AI tool or integration, working with the relevant control functions
Operate within the AI governance framework; provide technical evidence and control documentation to Compliance, Risk, Security, and the DPO as required
Maintain awareness of AI regulatory developments, including the EU AI Act timetable, and flag implications to the Data & AI Lead and Compliance
Work with Operations to review existing SOPs and identify where automation can reduce manual effort, error risk, or processing time
Where automation is not yet appropriate, document improved manual processes that are automation-ready
Measure and report on automation impact — hours saved, error reduction, throughput improvements