The Role
Build rapid prototypes and production-grade MVPs using JavaScript or TypeScript, React, databases, APIs, containers, and cloud platforms. Own deployment, CI/CD, monitoring, documentation, demo stability, and iterative client-driven enhancements. Collaborate with AI solution leads, designers, and analysts while evaluating third-party tools. Preferred experience includes vector databases, LLM APIs, RAG pipelines, Redis, cloud services, and AI orchestration frameworks.
Summary Generated by Built In
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.
Skills Required
- Strong proficiency with modern JavaScript or TypeScript, including production experience with React and Next.js or Express.js.
- Working experience with MongoDB and PostgreSQL, including schema design, indexing, query optimization, and migrations.
- Demonstrated experience deploying and operating applications in live environments using Docker, environment configuration, and cloud or PaaS deployment.
- Proficiency with Git and GitHub, GitLab, or Bitbucket, including branching strategies, pull requests, merge conflict resolution, and CI/CD integration.
- Ability to design, build, document, and secure RESTful APIs.
- Hands-on experience with vector databases such as Pinecone, Qdrant, Chroma, or pgvector.
- Experience with Tailwind CSS, Redux Toolkit or React Query, and component-driven development.
- Experience with asynchronous processing, job queues, Redis caching, and webhook handling.
- Familiarity with AWS, Azure, or GCP core services.
- Working knowledge of LLM APIs, including prompt construction, streaming responses, token management, and cost management.
- Experience building retrieval-augmented generation pipelines.
- Familiarity with LangChain, LlamaIndex, or agentic patterns.
- Exposure to Python for machine learning workflows or integration of Python ML services with Node.js applications.
- Understanding of machine learning concepts including model evaluation, embeddings, fine-tuning trade-offs, and inference cost.
- Bachelor's degree in Computer Science, Information Technology, Engineering, or equivalent practical experience.
- Three to five years of hands-on full stack development experience, including taking at least one application from zero to live deployment.
- Prior experience in a startup, product studio, innovation lab, or fast-paced consulting environment.
- Public portfolio, GitHub profile, or side projects demonstrating independent software development.
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The Company
What We Do
Flatworld Solutions is a global IT, business consulting, and outsourcing firm founded in 2004. As a diversified BPO and technology services provider, it supports over 18,000 clients across 100 countries. The company leverages automation and Generative AI to help enterprises streamline operations, enhance efficiency, and achieve transformative growth through a wide range of professional services and IT solutions.
