Backend Engineer

Backend Engineering and Architecture

• Design and implement low-latency, high-availability, and performant backend services and APIs that

power AI-enabled products.

• Be the architect for your module; own the design, scalability, and reliability of backend systems end to

end.

• Write reusable, testable, and efficient code; enforce engineering best practices across the team.
• Integrate user-facing elements developed by front-end developers with robust server-side logic and

AI-powered workflows.

• Implement security, data protection, and compliance standards across all backend services.
• Integrate multiple data sources, databases, and third-party systems into unified, scalable backend

architectures.

AI-Native Development

• Build, maintain, and optimise AI-native backend applications leveraging Large Language Models

(LLMs), Retrieval-Augmented Generation (RAG), AI agents, orchestration frameworks, and modern AI

platforms.

• Design and implement robust data pipelines, vector databases, embedding strategies, knowledge

retrieval systems, and model evaluation frameworks.

• Develop scalable backend workflows and integrations that automate business processes and deliver

measurable AI-driven value.

• Implement observability, monitoring, prompt management, testing pipelines, and guardrails to ensure

AI system quality, reliability, and compliance.

• Optimise AI application performance, latency, scalability, and cost efficiency across cloud

environments.

• Evaluate, experiment with, and integrate new AI models, tools, and frameworks to continuously

enhance product capabilities.

Requirements:

• 4+ years of experience in backend software engineering, with a strong focus on microservices design

and implementation.

• Hands-on experience building AI/ML or generative AI applications using technologies such as OpenAI,

Anthropic, Gemini, Azure AI, LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.

• Strong proficiency in Python (mandatory); familiarity with TypeScript, Java, or Go is a plus.
• Experience implementing RAG architectures, vector databases, embeddings, semantic search, prompt

engineering, and AI evaluation frameworks.

• Deep understanding of Large Language Models (LLMs), AI agents, model orchestration, and modern

AI development practices.

• Solid understanding of software architecture, APIs, microservices, distributed systems, and system

integrations.

• Strong grasp of algorithms, problem-solving, and fundamental design principles behind scalable

applications.

• Experience working with AWS, Azure, or GCP and deploying AI workloads in production environments.