AI Solutions Architect (Hands on)

Drive executive-level digital transformation for a global enterprise. Based directly within the Group CEO’s office, this high-autonomy role focuses on building production‑grade AI tools, automated workflows, and RAG pipelines to accelerate strategic decision‑making. If you have minimum 6+ years of experience deploying generative models

about the company.

Our client is a global enterprise embedding advanced AI capabilities across its international operations, treating artificial intelligence as mission‑critical, highly secure, and enterprise‑grade software. They offer a highly autonomous, execution‑focused position embedded directly within the Group CEO’s office to design and deploy practical AI tooling that accelerates executive productivity and streamlines strategic decision‑making across global markets.

about the team.

The successful candidate will collaborate closely with internal IT and Technical Infrastructure divisions to ensure all builds align with corporate architecture, security governance, and operational support standards, while delivering immediate, high‑value technical assets directly to the Group CEO.

about the job.

In this role, you will serve as the principal technical architect and builder for the executive office, creating automated tools that eliminate information lag and empower leadership with rapid insights.

• Design and implement autonomous agents and automated pipelines to extract, structure, and synthesise data from disparate internal networks, corporate databases, and external market feeds.
• Construct low‑latency executive interfaces, reporting tools, and intuitive, clean user interfaces tailored for C‑suite interaction.
• Build and sustain robust backend services, data infrastructure, and platform components capable of supporting AI workloads at scale.
• Integrate practical forecasting methods and lightweight machine learning modelling where they measurably sharpen decision accuracy.
• Implement modern software practices, including strict version control, comprehensive testing, automated CI/CD pipelines, environment provisioning, and secrets management.
• Establish and manage MLOps/LLMOps deployment pipelines for packaging, tracking, monitoring, and rolling back live models.
• Turn abstract executive requests into rapid, bespoke proofs‑of‑concept, maturing successful iterations into fully supported enterprise services.
• Navigate standard governance channels to seamlessly transition ownership of operational ERP systems to central engineering teams while maintaining ongoing technical ownership of executive‑specific tools.
• Partner with internal Cybersecurity and Infrastructure teams to build comprehensive runbooks, incident response protocols, and long‑term support models.
• Enforce strict data protection, access controls, and environment isolation to protect highly sensitive corporate data.
• Continually refine system architecture to maximise performance, uptime, and cost‑efficiency.

skills & experience required.

• Minimum 6+ years of experience as a software, platform, DevOps, or data engineer with a proven track record of shipping production‑grade applications.
• Hands‑on AI/LLM expertise, including deploying generative models into active enterprise systems.
• Proven experience with agentic frameworks, Retrieval‑Augmented Generation (RAG), vector databases, and API tool orchestration.
• Advanced backend development skills, specifically building robust APIs and data pipelines that handle diverse documents, structured analytics, and external data streams.
• Expertise in Python for backend application development, data manipulation, and workflow automation, alongside comfort using foundational statistical or machine learning techniques.
• Cloud infrastructure proficiency with AWS, Azure, or GCP, specifically around deploying, scaling, and maintaining live production environments.
• Strong software discipline, including modern CI/CD, infrastructure‑as‑code, automated testing, and foundational MLOps/LLMOps workflows.
• Commitment to security and architecture compliance, ensuring systems are built safely when handling highly confidential or operational data.
• A degree in Computer Science, Engineering, Mathematics, Data Science, or an equivalent technical field.
• Fluency in written and spoken English is mandatory
• Familiarity with advanced MLOps/LLMOps tools (e.g., MLflow, Kubeflow, model registries, and automated evaluation frameworks) is preferred.
• Experience utilising containerisation and orchestration layers (Docker, Kubernetes) is a plus.
• Prior experience designing digital tools for corporate executives, commodities trading, market research, or heavily regulated sectors is desirable.

skills

no additional skills required

qualifications

education

Bachelor Degree