Description

Key Responsibilities

As the AI Solutions Engineer, Office of the Group CEO, you will be the primary technical partner responsible for building practical AI tools and workflows that reduce information latency and support faster strategic decisions.

This is a builder role focused on turning high-level goals into working tools, prototypes and production-ready services using LLMs, agentic AI, data engineering and strong software discipline. It is not an architecture-only or research role; success is measured by reliable solutions that work day-to-day. Your day will typically include:

1. Agentic AI & Intelligent Data Extraction

1. Build Autonomous Agents : Design and deploy AI agents and automated data pipelines to extract, structure, and synthesize complex insights across internal corporate systems and external platforms.
2. Deliver Executive Insights : Construct low-latency dashboards, unified visual interfaces, and reporting tools that aggregate cross-functional data to directly support the CEO's decision-making.

2. Production-Grade Engineering & Infrastructure

1. Scale Backend Services : Architect and maintain robust backend infrastructure, platform components, and data pipelines optimized for high-volume AI workloads.
2. Apply Practical ML : Implement data analytics, forecasting models, and light machine learning where they materially elevate enterprise decision quality.
3. Enforce Best Practices : Drive rigorous software engineering standards, including CI/CD, automated testing, version control, infrastructure automation, and clear technical documentation.
4. Ensure Alignment : Guarantee all data and AI solutions fully comply with group technology standards, security frameworks, and architectural guardrails.

3. Rapid Prototyping & Pipeline Management (MLOps/LLMOps)

1. CEO Innovation Lab : Rapidly build and test bespoke prototype tools for the CEO, successfully maturing high-performing experiments into production-grade enterprise services.
2. Manage LLM Lifecycles : Operate MLOps/LLMOps pipelines for model packaging, version control, deployment, live monitoring, and rollback mechanisms.
3. Navigate Governance : Coordinate change management through proper governance channels for integrations involving PARIS (the in-house ERP), transitioning tools to operational ownership while retaining management over specific applications.

4. Security, Reliability & Scale

1. Secure by Design : Safeguard sensitive commercial and operational data by enforcing strict access control, data protection protocols, and environment segregation.
2. Operational Resilience : Collaborate with the central Technology, Cybersecurity, and Operations teams to define runbooks, support models, and incident response workflows.
3. Optimize Performance : Continuously enhance platform robustness, processing speed, cloud architecture, and cost efficiency.

Stakeholder Relations

1. Internal :
2. Office of the CEO : Align directly on strategic priorities, prototyping needs, and rapid product delivery.
3. Partner on corporate tech standards, security, architecture, and transitioning
4. Cross-Functional Stakeholders : Collaborate with business, finance, research, and operational teams to define problems, validate data, and drive tool adoption.
5. External :
6. Cloud & AI Vendors : Navigate platforms across AWS, Azure, and Google Cloud.
7. Specialist Data Providers : Integrate high-consequence external data feeds fromBloomberg, Clarksons, S&P Global , and other maritime or financial networks.

Job Experience & Qualifications

Essential Requirements

1. Experience :6+ years of hands-on experience as a software, platform, AI, or data engineer, with a proven track record of shipping production-grade systems.
2. AI & LLM Expertise : Practical experience deploying LLMs in real-world systems, with deep familiarity inagent frameworks, RAG (Retrieval-Augmented Generation), vector stores, and tool orchestration .
3. Backend Mastery : StrongPython programming skills for backend services, automated workflows, and data handling. Proven experience building robust APIs and multi-source data pipelines.
4. Cloud & DevOps : Hands-on cloud engineering experience (AWS, Azure, or GCP) deploying live services. Solid grounding in modern CI/CD, testing, version control, and core MLOps/LLMOps disciplines.
5. C-Suite Visualization : Ability to design clear, intuitive web interfaces and data visualizations that translate complex metrics for senior executives.
6. Education : Bachelor's degree in Computer Science, Data Engineering, Mathematics, or a highly technical STEM discipline.

Desirable Assets

1. Proficiency with dedicated MLOps/LLMOps tooling (e.g., MLflow, Kubeflow, model registries, evaluation pipelines).
2. Experience with containerization and orchestration workflows (Docker, Kubernetes ).
3. Prior experience building tools tailored for executive leadership, trading desks, research analysts, or high-consequence decision environments.
4. Exposure to highly regulated, operationally critical environments or specialized maritime, logistics, and commodities market data.