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.