Design and build scalable Generative AI applications, such as RAG systems and autonomous AI agents, using Python and frameworks such as LangChain or Semantic Kernel.
Set up cloud infrastructure on Microsoft Azure or AWS to ensure models run securely and efficiently.
Build production-ready GenAI service platforms.
Design reusable agent architectures that can scale across the organisation.
Integrate LLMs and agentic frameworks into existing engineering workflows.
Define best practices for agent design, testing, and observability.
Transform rapid AI experiments and prototypes into production-grade capabilities.
👨💻 What You Bring
At least 3 years of professional experience as an AI Engineer, ML Engineer, or Software Engineer, with a strong software engineering focus.
Strong Python skills and solid experience with cloud platforms.
Experience setting up Docker containers and CI/CD pipelines.
Hands-on experience building AI agents is essential.
End-to-end software engineering experience across the full lifecycle: architecture, implementation, testing, deployment, and production.
Comfortable working with LLMs, prompt engineering, and agentic frameworks such as LangChain, AutoGen, CrewAI, or similar technologies.
A polyglot programming mindset, with experience or willingness to work with languages such as Python, Java, Scala, etc.
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