Senior Full Stack Developer- Generative AI

About Us

Walnut Folks Group is the parent company of three agencies shaping how brands grow in India:

- Confluencr – influencer marketing (700+ brands served)

- Intent Farm – performance and marketplace marketing

- Kalakaaar – talent representation

About the Role

We're looking for a Senior Full Stack Developer – Generative AI who can take a problem from idea to production.

This isn't a role where you'll simply receive technical specifications and implement tickets. You'll work closely with founders, business teams, and product stakeholders to identify opportunities, shape solutions, build applications, and own them after launch.

You'll work across our SaaS products, Creator App, and internal AI-powered systems. Depending on the problem, you might build a customer-facing feature, develop an AI-powered workflow, or turn a manual business process into a scalable application.

We value engineers who combine solid software fundamentals with practical AI expertise, move quickly without compromising reliability, and take responsibility for the outcomes of what they build.

What You'll Do

1. Own Products End to End

• Understand business problems by working directly with founders, marketing teams, operations, finance, and other stakeholders.
  

• Translate requirements into clear technical plans, define MVPs, and prioritize features based on business impact.
  

• Build and ship complete features across frontend, backend, databases, APIs, and AI integrations.
  

• Take ownership of applications throughout their lifecycle, from initial prototype to production, monitoring, and continuous improvement.
  

2. Build AI-Powered Products

• Integrate LLMs and generative AI capabilities into real-world products and business workflows.
  

• Build AI-powered features using techniques such as structured outputs, tool calling, retrieval-augmented generation (RAG), and agentic workflows where appropriate.
  

• Evaluate models, prompts, and architectures to balance quality, latency, reliability, and cost.
  

• Design workflows that combine AI with business rules, databases, and external APIs.
  

• Implement appropriate validation, error handling, observability, and human review for AI outputs.
  

3. Develop Scalable Full-Stack Applications

• Build responsive, user-friendly frontend experiences and reliable backend services.
  

• Design APIs, data models, and database schemas that support product requirements and future growth.
  

• Write maintainable, testable code and follow sound engineering practices.
  

• Integrate third-party services, authentication systems, background jobs, and other infrastructure as needed.
  

• Identify and resolve performance bottlenecks, bugs, and production issues.
  

4. Ship, Measure, and Improve

• Prototype promising ideas quickly and validate them with actual users.
  

• Deploy applications and manage releases, monitoring, and ongoing maintenance.
  

• Use user feedback, product usage, and business outcomes to guide improvements.
  

• Leverage AI coding tools to accelerate development while maintaining code quality, security, and reliability.
  

• Document key technical decisions and share learnings with the team.
  

What We're Looking For

Essential Skills

• 2–4 years of experience building and shipping full-stack software applications.
  

• Strong programming fundamentals and hands-on experience developing frontend and backend applications.
  

• Practical experience integrating LLMs or generative AI into applications, workflows, or developer tooling.
  

• Experience designing and working with REST APIs, databases, and third-party integrations.
  

• Familiarity with deploying applications to production and troubleshooting real-world issues.
  

• Ability to break down ambiguous requirements into practical, well-scoped solutions.
  

• A strong sense of ownership, with a focus on usability, reliability, and business outcomes.
  

• We are particularly interested in candidates who have gone beyond basic chatbot implementations.
  

• Working with LLM APIs and structured model outputs.
  

• Building RAG pipelines, semantic search, or document-processing workflows.
  

• Implementing AI agents, tool calling, or multi-step AI workflows where appropriate.
  

• Evaluating AI output quality and handling hallucinations, failures, and edge cases.
  

• Using AI coding assistants to improve development speed and productivity.
  

• Experience building consumer-facing applications or SaaS products with active users.
  

• Experience with cloud infrastructure, CI/CD, automated testing, and application monitoring.
  

• Experience with background processing, queues, caching, or event-driven systems.
  

• Experience in marketing technology, creator platforms, analytics, or workflow automation.
  

• A strong portfolio, GitHub repository, live product, or technical case study demonstrating your work.
  

You don't need experience with every technique listed. We value practical implementation experience and sound technical judgment over familiarity with buzzwords.

How You Work Matters

• You can communicate technical trade-offs to non-technical stakeholders.
  

• You are comfortable making decisions when requirements are incomplete.
  

• You know when to use AI, when traditional software is more reliable, and when a simpler solution is better.
  

• You can move fast, accept feedback, and iterate without losing sight of code quality.
  

• You take responsibility for what happens after deployment, not just for writing the code.
  

Our Technology Environment

We care more about your ability to choose the right tools and build reliable products than about matching every item on a checklist.

Depending on the product, our engineering environment may involve modern frontend frameworks, backend services, relational or NoSQL databases, cloud infrastructure, LLM APIs, and AI development tools.

You'll have the opportunity to evaluate technologies, experiment with new approaches, and make informed architectural decisions.

How We'll Measure Success

In this role, success means:

• Turning business problems into working solutions that users can actually use.
  

• Shipping useful prototypes and production features with appropriate speed and quality.
  

• Building AI capabilities that deliver measurable improvements in accuracy, productivity, or user experience.
  

• Maintaining reliable applications through testing, monitoring, and continuous improvement.