Senior GenAI Application Analyst (Contract)

Your tasks

• 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.
• Engineering solutions for production quality, including logging, tracing, evaluation, fallback behaviour and troubleshooting.

Requirements

• 5 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.
• Good understanding of API design, distributed systems and production resilience patterns.
• 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.

Nice to Have

• Experience with DeepAgent or similar agent frameworks.
• Experience with Langfuse, Elastic or similar observability and search tools.
• Experience with Redis for caching, conversation state, rate limiting, queue-backed workflows or low-latency GenAI application patterns.
• Experience with vLLM or similar inference serving frameworks for running Openweight models.
• Experienced in deploying GenAI workloads on cloud or container platforms.