Talentpool for Full Stack Engineer

An AI Full Stack Engineer (also known as Full-Stack AI Developer or AI Application Engineer) builds complete, end-to-end AI-powered applications. This role combines traditional full-stack development (frontend + backend) with AI/ML integration, enabling the creation of intelligent, production-ready products like AI chatbots, recommendation systems, intelligent dashboards, RAG applications, and agentic AI tools.

You will own features from UI/UX to data pipelines and model deployment — bridging software engineering, machine learning, and cloud infrastructure.

This is a high-demand, versatile role ideal for modern tech companies, startups, and enterprises adopting generative AI.

Key Responsibilities

• Design and develop responsive, user-friendly frontend interfaces using modern frameworks.
• Build robust backend services, APIs, and data layers to support AI functionalities.
• Integrate AI/ML models (LLMs, computer vision, recommendation engines) into production applications.
• Implement Retrieval-Augmented Generation (RAG), vector search, embeddings, and agentic workflows.
• Develop and optimize data pipelines that feed AI models (connecting to data lakes, warehouses, or real-time sources).
• Deploy, monitor, and scale AI applications using MLOps practices, containerization, and cloud services.
• Ensure performance, security, scalability, and ethical AI compliance (bias mitigation, data privacy).
• Collaborate with data scientists, ML engineers, designers, and product teams to deliver AI-driven features.
• Write clean, maintainable code and participate in code reviews, testing, and CI/CD pipelines.

Required Skills & Qualifications

• Frontend: React.js / Next.js, Vue, Angular, TypeScript, Tailwind CSS or similar.
• Backend: Python (FastAPI, Flask, Django), Node.js, or Go. Strong API development (REST, GraphQL).

AI/ML Integration

• Experience with LangChain, LlamaIndex, Hugging Face, OpenAI/Anthropic APIs.
• Basic model training/evaluation with PyTorch or TensorFlow.

Data & Infrastructure

• SQL/NoSQL databases, data lakes/lakehouses.
• Cloud platforms: AWS, Azure, GCP (especially AI services like Bedrock, SageMaker, Vertex AI).
• Other: Git, testing (unit + integration), observability (Prometheus, LangSmith).
• Experience: 3–7+ years in full-stack development, with at least 1–2 years in AI/ML integration.
• Education: Bachelor’s or Master’s in Computer Science, Software Engineering, or related field.

Nice-to-Have Skills

• Experience with LLMOps, AI agents, or multimodal AI.
• Domain knowledge in specific industries (healthcare, finance, e-commerce, etc.).
• Strong understanding of AI ethics, governance, and responsible AI practices.