Fullstack Developer + GenAI

Key Responsibilities

• Design, develop, and maintain full-stack web applications (frontend and backend) with clean, scalable, and maintainable code.
• Integrate generative AI / large language models (LLMs) into user-facing applications (chatbots, intelligent assistants, content generation, etc.).
• Build and maintain RESTful or GraphQL APIs for AI integration and data access.
• Work on data pipelines, ingestion, and preprocessing of structured and unstructured data.
• Use vector stores / embedding databases / semantic search (for RAG and other AI use cases).
• Develop promp engineering, fine-tuning, and model optimization for generative AI services.
• Ensure system reliability, monitoring, and observability of AI services in production.
• Collaborate with UX/design teams to build responsive and interactive frontends.
• Ensure security, data privacy, and compliance in AI features.
• Participate in code reviews, mentor junior engineers, and help define best practices.

Required Skills & Qualifications

• Bachelors degree in Computer Science, Engineering, or related field (or equivalent experience).
• At least

  3–5 years

  of experience in full-stack development.
• Strong proficiency in one or more backend languages / frameworks (e.g. Python - Flask, FastAPI, Django; Node.js; Java; etc.).
• Good frontend skills using React / Angular / Vue (JavaScript/TypeScript).
• Experience using cloud platforms (AWS, GCP, Azure) and working with containerization (Docker, Kubernetes).
• Experience with AI/ML frameworks or services; familiarity with LLMs, prompt engineering.
• Understanding of vector databases, embeddings, retrieval-augmented generation (RAG).
• Solid understanding of REST APIs, microservices architecture.
• Version control (Git), CI/CD pipelines, automated testing.

Preferred Skills

• Prior work deploying generative AI solutions in production.
• Experience with frameworks/libraries such as LangChain, LlamaIndex, etc.
• Experience with agent-based architectures or multi-agent systems.
• Skills in scaling and optimizing performance, latency, and resource usage.
• Familiarity with real-time data / streaming, graph databases.
• Experience in domain-specific AI (NLP, computer vision, or other relevant fields).