Associate Full Stack Engineer

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.