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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Full Stack Engineer - **Company:** Prolo - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Network Analysis, Cloud Engineering, Computer Programming, Data Validation, Extract Transform Load (ETL), Serialization, Cursor (Graphical User Interface Elements), Database Design, Software Debugging, DevOps, Github, Graph Database, Hypertext Transfer Protocols (HTTP), Python (Programming Language), PostgreSQL, Machine Learning, Neo4j, NoSQL, NumPy, Open Source Technology, Queueing Systems, Prometheus, Scientific Computating, Search Technologies, Software Engineering, SQL Databases, Systems Integration, Unstructured Data, Web Application Frameworks, Jupyter Notebook, GitHub Copilot, Large Language Models, Grafana, Prompt Engineering, Backend, Cloudformation, Fastapi, Data Layers, Pandas, Event Driven Architecture, Containerization, AI Platforms, Kubernetes, Web Technologies, Front End Software Development, Api Design, Api Gateway, Restful APIs, Terraform, Code Restructuring, Data Pipelines, Api Management, Serverless Computing, Docker, Microservices - **Published:** May 16, 2026 - **Apply:** https://find.jobs/jobs-near-me/full-stack-engineer-london/2770241960-2/ ## About the Role Software Development * 5+ years of professional software engineering experience * Strong proficiency in Python (3.10+) with deep understanding of async programming * Experience with Poetry or similar Python dependency management tools * Experience building RESTful APIs and micro-services * Solid understanding of database design and optimisation (both SQL and NoSQL) * Experience with graph databases (Neo4j preferred) or willingness to learn quickly * Knowledge of event-driven architectures and message queues * Knowledge of API design principles, data validation, and serialisation * Experience with AWS Lambda and serverless architectures * Experience working across the stack (backend + some frontend) * Understanding of web technologies, HTTP, and API integrations * Ability to contribute to responsive frontend code when needed * Hands-on experience using AI-assisted development tools (e.g. Cursor, GitHub Copilot) including prompt engineering, context management, and evaluating AI-generated code critically DevOps & Infrastructure * Hands-on experience with cloud platforms (AWS preferred) * Experience with containerisation (Docker) and orchestration (Kubernetes) * Knowledge of Infrastructure as Code (Terraform, CloudFormation, or similar) * Experience setting up CI/CD pipelines * Understanding of service deployment, monitoring, and troubleshooting Data Science/Analytics * Experience with data analysis using Python (pandas, numpy) * Understanding of data pipelines and ETL processes AI/LLM Engineering * Experience integrating LLM APIs (OpenAI, Anthropic, Gemini, or open-source equivalents) into production applications * Understanding of core LLM concepts: context windows, token limits, temperature, system prompts, and model selection trade-offs * Experience with prompt engineering techniques - few-shot prompting, chain-of-thought, structured output, and instruction tuning Soft Skills * Wearer of Many Hats: Comfortable switching contexts and working across different domains * Self-Starter: Ability to work independently and take ownership of projects * Problem Solver: Strong analytical and debugging skills * Collaborative: Excellent communication skills and ability to work in a small team * Adaptable: Comfortable with ambiguity and rapid iteration Nice-to-Have Qualifications * Experience with observability tools (OpenTelemetry, Prometheus, Grafana) * Familiarity with agentic coding workflows - using AI agents to scaffold, refactor, test, and document code autonomously * Experience with FastAPI or similar async Python web frameworks * Experience with Neo4j or other graph databases * Experience with graph algorithms and network analysis * Experience with Helm and Kubernetes operators * Background in data science, statistics, or scientific computing * Experience with graph analytics or network analysis * Experience with RAG, LLM Orchestration and MCP * Experience in early-stage startups ## Description We are seeking a Full-Stack Engineer to join our team and help build the next generation of AI solutions. This is a unique opportunity to be a key technical contributor in a fast-paced, innovative environment where you'll wear many hats and have significant impact on our product and engineering culture. As a full-stack engineer, you'll work across the entire technology stack-from backend services and data pipelines to infrastructure and deployment. You'll collaborate closely with the engineering team to architect, build, and scale our micro-services based platform while maintaining high code quality and operational excellence, utilising AI to maximise software development productivity. About Prolo Prolo is building an AI-powered procurement platform for the construction industry, which is one of the oldest and least digitised sectors in the world. The core system ingests unstructured purchase orders and transforms them into structured materials data, integrating with a network of suppliers and logistics partners to automate quoting, sourcing, and fulfilment workflows. What We're Building We're building a sophisticated AI platform that leverages graph databases, machine learning, and modern cloud infrastructure to deliver intelligent procurement and customer service solutions. Our stack includes: * Backend Services: Python 3.13, FastAPI, async micro-services architecture * Data Layer: Neo4j graph database, PostgreSQL, complex data modelling * AI/ML: OpenAI integration, semantic search, conversational agents, unstructured data analysis and extraction * Infrastructure: AWS (Lambda, ECS/EKS, API Gateway, S3) * DevOps: Terraform, GitHub Actions, Helm, Infrastructure-as-Code * Data Science: Graph analytics, data pipelines, ETL workflows, Jupyter notebooks ## Related Videos - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [The Prompt Engineer ✍️](https://www.wearedevelopers.com/magazine/216-the-prompt-engineer) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)