Associate Full Stack Engineer

Key Responsibilities A. Rapid Prototyping \u2022 Build functional prototypes for multiple solution concepts in parallel \u2014 typically 2 to 4 week build cycles per idea. \u2022 Translate solution blueprints and wireframes from the AI Solutioning team into working, clickable applications. \u2022 Make pragmatic technical trade-offs: choose speed-to-demo over premature optimisation, while keeping the code clean enough to extend. \u2022 Rapidly evaluate and integrate third-party APIs, SDKs, and open-source components to avoid building from scratch. B. MVP Development & Deployment \u2022 Take one or two selected prototypes per cycle to production-grade MVP: authentication, data persistence, error handling, and responsive UI. \u2022 Own end-to-end deployment \u2014 containerise, configure environments, and deploy to cloud platforms (AWS, Azure, GCP). \u2022 Set up and maintain CI/CD pipelines so every MVP has a repeatable, one-command deploy path. \u2022 Ensure MVPs are demo-stable: seeded data, reliable uptime during pitch windows, and failure handling. \u2022 Instrument basic logging and monitoring so issues surfacing during a client demo can be diagnosed quickly. C. Client-Pitch Enablement (Build Support) \u2022 Prepare demo environments and walkthrough-ready builds ahead of client pitches; the AI Solutions Lead presents; you make sure it works. \u2022 Produce short technical notes and architecture diagrams the Lead can use to answer client questions during pitches. \u2022 Turn client feedback captured in pitch sessions into prioritised build tickets and rapid iterations. \u2022 Maintain a reusable component and boilerplate library so each new prototype starts further along. D. Engineering Practice & Collaboration \u2022 Maintain disciplined version control: feature branching, meaningful commit history, pull requests, and code review participation. \u2022 Write concise technical documentation \u2014 setup instructions, environment variables, API contracts, and deployment runbooks. \u2022 Collaborate closely with business analysts, designers, and the AI Solutions Lead in short, iterative cycles. \u2022 Contribute to internal accelerators and shared tooling that shorten the path from idea to demo. Requirements Mandatory Technical Requirements The following are non-negotiable for this role: \u2022 JavaScript / TypeScript: Strong proficiency with modern JS/TS. Hands-on production experience with React and at least one of Next.js or Express.js. [MANDATORY] \u2022 Databases: Working experience with MongoDB and PostgreSQL \u2014 schema design, indexing, query optimisation, and migrations. [MANDATORY] \u2022 Application Deployment: Demonstrated experience deploying and running applications in a live environment \u2014 containerisation (Docker), environment configuration, and cloud or PaaS deployment. [MANDATORY] \u2022 Version Control: Proficiency with Git and GitHub (or GitLab / Bitbucket) \u2014 branching strategies, pull requests, merge conflict resolution, and CI/CD integration. [MANDATORY] \u2022 REST API Development: Ability to design, build, document, and secure RESTful APIs. [MANDATORY] Strongly Preferred \u2022 Vector Databases: Hands-on experience with Pinecone, Qdrant, Chroma, or pgvector \u2014 embedding storage, similarity search, and retrieval tuning. \u2022 Frontend Depth: Tailwind CSS, state management (Redux Toolkit, or React Query), and component-driven development. \u2022 Backend Patterns: Asynchronous processing, job queues, caching (Redis), and webhook handling. \u2022 Cloud Services: Familiarity with AWS (EC2, S3, Lambda), Azure, or GCP core services. Advantageous (ML / AI Exposure) Not required, but a clear differentiator for this role: \u2022 Working knowledge of LLM APIs (OpenAI, Anthropic, Google) \u2014 prompt construction, streaming responses, token and cost management. \u2022 Experience building RAG pipelines: document chunking, embedding generation, and retrieval-augmented response flows. \u2022 Familiarity with orchestration frameworks such as LangChain, LlamaIndex, or agentic patterns. \u2022 Exposure to Python for ML workflows, or integrating Python ML services into a Node.js application. \u2022 Understanding of core ML concepts: model evaluation, embeddings, fine-tuning trade-offs, and inference cost. What We Look For (Beyond the Stack) \u2022 Bias toward shipping \u2014 you would rather have something working and imperfect than perfect and unbuilt. \u2022 Comfort with ambiguity: specifications will sometimes be a wireframe and a conversation. \u2022 Breadth over narrow specialisation; genuine curiosity about unfamiliar tools. \u2022 Ability to estimate honestly and flag scope risk early rather than late. \u2022 A public portfolio, GitHub profile, or side projects that show what you build when nobody assigns it. Qualifications \u2022 Bachelor\u2019s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience. \u2022 3 \u2013 5 years of hands-on full stack development experience with at least one application taken from zero to live deployment. \u2022 Prior experience in a startup, product studio, innovation lab, or fast-paced consulting environment is a plus. Benefits What We Offer \u2022 Variety \u2014 you will build across multiple domains and problem spaces rather than one product forever. \u2022 Direct line of sight from your code to a real client decision. \u2022 Freedom to pick the right tools for each prototype, within sensible guardrails. \u2022 Mentorship from the AI Solutions Lead and exposure to enterprise solutioning practice. \u2022 Learning budget for AI/ML upskilling and cloud certifications. \u2022 Competitive compensation with a clear path toward Senior Engineer or Solution Engineer tracks.