Key Responsibilities A. Rapid Prototyping • Build functional prototypes for multiple solution concepts in parallel — typically 2 to 4 week build cycles per idea. • Translate solution blueprints and wireframes from the AI Solutioning team into working, clickable applications. • Make pragmatic technical trade-offs: choose speed-to-demo over premature optimisation, while keeping the code clean enough to extend. • Rapidly evaluate and integrate third-party APIs, SDKs, and open-source components to avoid building from scratch. B. MVP Development & Deployment • Take one or two selected prototypes per cycle to production-grade MVP: authentication, data persistence, error handling, and responsive UI. • Own end-to-end deployment — containerise, configure environments, and deploy to cloud platforms (AWS, Azure, GCP). • Set up and maintain CI/CD pipelines so every MVP has a repeatable, one-command deploy path. • Ensure MVPs are demo-stable: seeded data, reliable uptime during pitch windows, and failure handling. • Instrument basic logging and monitoring so issues surfacing during a client demo can be diagnosed quickly. C. Client-Pitch Enablement (Build Support) • Prepare demo environments and walkthrough-ready builds ahead of client pitches; the AI Solutions Lead presents; you make sure it works. • Produce short technical notes and architecture diagrams the Lead can use to answer client questions during pitches. • Turn client feedback captured in pitch sessions into prioritised build tickets and rapid iterations. • Maintain a reusable component and boilerplate library so each new prototype starts further along. D. Engineering Practice & Collaboration • Maintain disciplined version control: feature branching, meaningful commit history, pull requests, and code review participation. • Write concise technical documentation — setup instructions, environment variables, API contracts, and deployment runbooks. • Collaborate closely with business analysts, designers, and the AI Solutions Lead in short, iterative cycles. • 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: • 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] • Databases: Working experience with MongoDB and PostgreSQL — schema design, indexing, query optimisation, and migrations. [MANDATORY] • Application Deployment: Demonstrated experience deploying and running applications in a live environment — containerisation (Docker), environment configuration, and cloud or PaaS deployment. [MANDATORY] • Version Control: Proficiency with Git and GitHub (or GitLab / Bitbucket) — branching strategies, pull requests, merge conflict resolution, and CI/CD integration. [MANDATORY] • REST API Development: Ability to design, build, document, and secure RESTful APIs. [MANDATORY] Strongly Preferred • Vector Databases: Hands-on experience with Pinecone, Qdrant, Chroma, or pgvector — embedding storage, similarity search, and retrieval tuning. • Frontend Depth: Tailwind CSS, state management (Redux Toolkit, or React Query), and component-driven development. • Backend Patterns: Asynchronous processing, job queues, caching (Redis), and webhook handling. • 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: • Working knowledge of LLM APIs (OpenAI, Anthropic, Google) — prompt construction, streaming responses, token and cost management. • Experience building RAG pipelines: document chunking, embedding generation, and retrieval-augmented response flows. • Familiarity with orchestration frameworks such as LangChain, LlamaIndex, or agentic patterns. • Exposure to Python for ML workflows, or integrating Python ML services into a Node.js application. • Understanding of core ML concepts: model evaluation, embeddings, fine-tuning trade-offs, and inference cost. What We Look For (Beyond the Stack) • Bias toward shipping — you would rather have something working and imperfect than perfect and unbuilt. • Comfort with ambiguity: specifications will sometimes be a wireframe and a conversation. • Breadth over narrow specialisation; genuine curiosity about unfamiliar tools. • Ability to estimate honestly and flag scope risk early rather than late. • A public portfolio, GitHub profile, or side projects that show what you build when nobody assigns it. Qualifications • Bachelor’s degree in Computer Science, Information Technology, Engineering, or equivalent practical experience. • 3 – 5 years of hands-on full stack development experience with at least one application taken from zero to live deployment. • Prior experience in a startup, product studio, innovation lab, or fast-paced consulting environment is a plus. Benefits What We Offer • Variety — you will build across multiple domains and problem spaces rather than one product forever. • Direct line of sight from your code to a real client decision. • Freedom to pick the right tools for each prototype, within sensible guardrails. • Mentorship from the AI Solutions Lead and exposure to enterprise solutioning practice. • Learning budget for AI/ML upskilling and cloud certifications. • Competitive compensation with a clear path toward Senior Engineer or Solution Engineer tracks.