We are seeking a motivated Software Solution Specialist with 1–2 years of experience in enterprise business application environments to join our AI solution team.  In this role, you will support the migration of legacy applications and data pipelines to modern AI-powered platforms, integrating frontier AI services to enhance enterprise workflows.  You will work closely with business stakeholders, data engineers, and security teams to deliver scalable, compliant, and intelligent solutions operating primarily on Linux-based infrastructure.

Responsibilities:

• Analyze and document existing legacy business application architectures and data flows to identify migration and AI integration candidates.
• Design and implement migration plans for legacy systems to modern cloud-based AI analytics platforms (e.g. Microsoft, AWS and Google), applying appropriate frontier AI services as needed.
• Develop and maintain data pipelines, ETL processes, and integrations between enterprise business applications and AI-powered solutions on Linux environments.
• Administer and support Linux-based application servers, ensuring stability, performance, and security during and after migration activities.
• Collaborate with security and compliance teams to ensure GDPR and enterprise data governance standards are met throughout the migration.
• Assist in evaluating and integrating AI/ML capabilities such as LLM APIs and retrieval-augmented generation (RAG) solutions into enterprise business workflows.
• Support end-users and stakeholders during transition periods, providing technical guidance and training as needed.
• Contribute to defining best practices for AI-assisted development tooling usage within the team.

Requirements:

• Degree in Computer Science, Information Technology, Cyber Security or the equivalent with minimum 1 year of relevant experience in IT, software solution and in-house application support
• 1–2 years of hands-on experience supporting or developing enterprise business applications (ERP, CRM, or custom business systems).
• Foundational understanding of self-hosted LLM concepts and deployment environments — including on-premises, private cloud, and air-gapped infrastructure — with a willingness to build hands-on operational experience. Core knowledge areas include:
  • LLM fundamentals and inference concepts — basic understanding of how large language models work, the role of inference servers (e.g. Ollama, llama.cpp, vLLM), and how model parameters, quantisation levels (e.g. GGUF, GPTQ), and context window size affect hardware requirements and output quality
  • Linux system and hardware awareness — practical familiarity with Linux-based server environments, including process management, file system operations, and basic understanding of how CPU, GPU, RAM, and storage interact with compute-intensive workloads such as model inference
  • Prompt engineering and RAG basics — conceptual familiarity with prompt construction, retrieval-augmented generation (RAG) patterns, and how locally hosted models can be integrated with enterprise data sources via APIs or scripting (e.g. Python with LangChain or similar frameworks)
• Practical experience with enterprise business applications; experience with comparable enterprise systems or platforms is an added advantage:
  • ERP systems (e.g., Oracle E-Business Suite, SAP, or equivalent)
  • CRM or workflow management platforms
  • Enterprise reporting and BI tools
• Scripting or development proficiency to support automation and integration (Python, Bash, or similar).
• Familiarity with SQL and relational database concepts in enterprise environments.
• Understanding of ETL/ELT concepts and data pipeline development.