- Job Title
- AI Technical Expert
Job Description
Role Summary
We are looking for a senior technology professional with 15+ years of experience in software engineering, architecture, and enterprise technology, with strong experience across data engineering, machine learning, and AI/Generative AI.
The candidate will be responsible for applying AI to solve meaningful enterprise problems and realize measurable business and engineering value, while architecting and delivering scalable AI-enabled solutions and reusable AI capabilities.
The role requires strong software and data engineering foundations, AI/ML expertise, system architecture, hands-on development, and technical leadership.
Key Responsibilities
Identify and realize high-value enterprise opportunities through AI, with measurable business and engineering impact.
Architect and develop AI/ML and Generative AI solutions for enterprise use cases.
Build reliable data pipelines, data platforms and data processing capabilities that support AI/ML solutions.
Apply ML techniques to develop, train, evaluate and deploy machine learning solutions.
Design and develop LLM-based applications, including RAG, agents, tool use, and AI-assisted workflows.
Integrate AI solutions with existing enterprise platforms, APIs, data sources, and engineering tools.
Establish practices for AI architecture, evaluation, security, observability, and governance.
Lead POCs from hypothesis through productionisation, with measurable outcomes.
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Mentor engineers and establish engineering best practices.
Experience & Skills
15+ years of software engineering / architecture / technology experience.
Strong programming skills, particularly Python, and solid software architecture fundamentals.
Experience designing data pipelines, data processing workflows, data platforms, and data quality practices.
Practical experience with ML model development lifecycle including data processing, ML model training, evaluation, deployment, and monitoring.
Strong understanding of AI/ML techniques and modern GenAI approaches, including but not limited to LLMs, prompting, context engineering and inference.
Practical experience with LLMs, RAG, AI agents, embeddings, and vector databases.
Experience with modern AI application frameworks and orchestration technologies such as LangChain, LangGraph, Semantic Kernel or equivalent.
Experience taking AI/ML solutions from PoC to production in enterprise environments.
Understanding of AI security, evaluation, observability, and governance.
Experience applying AI across the software development lifecycle (SDLC), DevOps and developer productivity is highly desirable.
