Job Title

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.
• 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.