Senior Software Engineer - AI

We are looking for a highly skilled Senior Software Engineer - AI to design, build, and deploy production-grade Agentic AI systems. In this role, you will work on Large Language Models (LLMs), multi-agent architectures, prompt engineering, retrieval systems, and AI orchestration frameworks to deliver scalable and reliable AI solutions. You will collaborate closely with Product Managers, Engineering teams, and AI researchers to build intelligent systems that automate complex workflows and solve real business problems.

Responsibilities

• Design, develop, and deploy production-ready AI applications powered by Large Language Models (LLMs).
• Build and maintain agentic AI workflows using orchestration frameworks.
• Develop prompt engineering strategies and optimise AI model performance.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines and memory architectures.
• Build multi-agent systems for planning, execution, validation, and decision-making.
• Integrate AI agents with internal and external tools using APIs and function calling.
• Implement monitoring, evaluation, and observability for AI applications.
• Optimise AI systems for latency, reliability, scalability, and inference costs.
• Troubleshoot production issues related to LLM behaviour, hallucinations, tool failures, and model performance.
• Collaborate with cross-functional teams to translate business requirements into AI-powered solutions.
• Stay up to date with the latest advancements in Generative AI, Agentic AI, and LLM technologies.
  
  

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 4-8 years of software engineering experience.
• Minimum 2 years of hands-on experience building AI/LLM-based applications.
• Strong programming skills in Python or TypeScript.
• Experience working with LLMs such as OpenAI GPT, Claude, Gemini, or similar models.
• Strong understanding of Prompt Engineering and AI application development.
• Experience building AI agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or similar.
• Hands-on experience with Retrieval-Augmented Generation (RAG), vector databases, and embedding models.
• Experience integrating APIs, tools, and external services with AI systems.
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with Docker, Kubernetes, CI/CD pipelines, and Git.
• Strong problem-solving and debugging skills.
  
  

• Experience building production-scale multi-agent AI systems.
• Knowledge of AI evaluation frameworks and LLM testing methodologies.
• Experience with observability tools for AI applications.
• Understanding of model routing, fine-tuning, and inference optimisation.
• Experience with distributed systems and microservices architecture.
• Contributions to open-source AI projects or personal AI applications are a plus.