Senior Backend Developer (Generative AI & LLM Systems)

Role Overview

• We are looking for a Senior Backend Developer with deep expertise in Generative AI and LLM Systems to
• design, build, and scale the intelligent backbone of our products. You will bridge solid software
• engineering fundamentals with cutting-edge AI capabilities — owning everything from RESTful API design
• and system architecture through to LLM orchestration, multi-agent workflows, and RAG pipelines. You will
• work closely with product, data science, and front-end teams, bringing both technical depth and a
• pragmatic, delivery-focused mindset to every project.

Key Responsibilities

• Design, develop, and deploy applications powered by LLMs and generative AI models
• Engineer, test, and optimize prompts for performance, accuracy, and reliability
• Build and manage multi-agent systems for complex task orchestration
• Integrate AI models with APIs, databases, and external tools
• Implement retrieval-augmented generation (RAG) pipelines
• Fine-tune and evaluate models using domain-specific datasets
• Monitor model outputs, ensure alignment, and reduce hallucinations
• Collaborate with product, data, and engineering teams to deliver AI-driven features

Required Skills & Qualifications

• Architect and develop scalable, production-grade backend services and APIs that power AI-driven features.
• Design and implement LLM orchestration pipelines using frameworks such as LangChain, LlamaIndex, or custom-built solutions.
• Build, evaluate, and maintain multi-agent systems capable of handling complex, multi-step task orchestration.
• Engineer, test, and continuously optimise prompts for accuracy, reliability, consistency, and cost efficiency.
• Implement and maintain Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding, and retrieval strategies.
• Integrate AI models with internal and third-party APIs, vector databases, relational databases, and external tools.
• Fine-tune, evaluate, and monitor LLMs using domain-specific datasets; establish robust evaluation frameworks to track model quality over time.
• Implement guardrails, safety checks, and alignment strategies to reduce hallucinations and ensure responsible AI outputs.
• Proactively identify performance bottlenecks, technical debt, and opportunities to improve system reliability and developer experience.
• 5–7 years of professional backend / full-stack development experience, with at least 2 years focused on AI or ML systems.
• Strong Python proficiency — clean, testable, production-ready code.
• Deep, hands-on expertise in prompt engineering: chain-of-thought, few-shot, system prompts, structured outputs, and tool use.
• Proven experience building with LLMs in production — OpenAI (GPT-4o / o-series), Anthropic Claude, Mistral, LLaMA, or similar.
• Solid understanding of agentic patterns: ReAct, plan-and-execute, tool-calling agents, and agent memory strategies.
• Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent).
• Experience designing and operating RAG pipelines end-to-end, including chunking strategies, embedding models, and re-ranking.
• Familiarity with vector databases and embeddings (Pinecone, Weaviate, Qdrant, FAISS, pgvector, or similar).
• Strong API design skills — RESTful services, versioning, authentication, rate limiting, and error handling.
• Experience with relational and NoSQL databases (PostgreSQL, MongoDB, Redis) and their use alongside AI workloads.
• Understanding of AI evaluation metrics (RAGAS, LLM-as-judge, latency, cost, groundedness) and model performance tuning.
• Comfortable working in cloud environments (AWS / GCP / Azure) and containerised deployments (Docker, Kubernetes).

Preferred Qualifications

• Experience fine-tuning LLMs (LoRA/QLoRA, SFT, RLHF) or training custom models on domain-specific datasets.
• Exposure to MLOps tooling — experiment tracking (MLflow, W&B), model registries, and automated evaluation pipelines.
• Experience building conversational AI products: chatbots, copilots, voice assistants, or similar.
• Knowledge of AI safety, guardrails, and governance frameworks (e.g., content moderation, PII detection, output filtering).
• Familiarity with streaming responses, WebSockets, or Server-Sent Events for real-time AI interfaces.
• Contributions to open-source AI projects or a portfolio demonstrating AI system design.
• Exposure to graph databases or knowledge graphs as a complement to vector retrieval.

TECHNOLOGY STACK

Core technologies you will work with day-to-day

• Python • Docker & Kubernetes
• OpenAI / Anthropic / OSS LLMs • AWS / GCP / Azure
• LangChain / LlamaIndex • GitHub Actions (CI/CD)
• Pinecone / pgvector / FAISS • Prompt engineering & eval frameworks
• PostgreSQL & Redis • MLflow / Weights & Biases