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 workplace 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.

📌 Associate Full Stack Engineer (Bengaluru)
🏢 Flatworld Solutions
📍 Bengaluru