Senior GenAI Engineer

Responsibilities:

• Build and improve GenAI applications using frameworks such as LangGraph, LangChain or similar orchestration tools.
• Work with open weight models, hosted LLMs and model serving patterns.
• Develop agentic workflows, retrieval augmented generation, tool calling and prompt orchestration.
• Integrate GenAI applications with enterprise systems, APIs, data sources and operational platforms.
• Engineer solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.
• Strong hands on experience building production grade GenAI applications.
• Practical experience with LangGraph, LangChain, agentic workflows, RAG and tool calling.
• Understanding of open weight models and how to integrate them into real applications.
• Ability to challenge weak designs and propose better ones.
• Strong engineering discipline around reliability, testing, observability and maintainability.
• Ability to work across application, data, platform, security and infrastructure teams.

Requirement:

• 10 or more years of software engineering experience, preferably with recent hands on experience in GenAI application development.
• Proven experience building production grade GenAI applications, not only prototypes, experiments or demos.
• Strong hands on experience with LangGraph, LangChain or similar orchestration frameworks.
• Practical understanding of RAG, agentic workflows, tool calling, prompt orchestration and context management.
• Experience integrating LLMs into real applications using APIs, backend services and enterprise data sources.
• Experience with open weight models or strong interest backed by hands on experimentation.
• Strong backend engineering skills using Python, Java or similar languages.
• Experience with observability, logging, tracing and troubleshooting for GenAI or backend applications.
• Familiarity with containerised deployment environments such as Kubernetes or OpenShift.
• Ability to write clean, maintainable and testable code.
• Strong analytical, debugging and troubleshooting skills.
• High ownership, high curiosity and genuine interest in building useful AI products.