Full Stack Agentic AI Engineer

We are looking for a passionate and hands-on Full Stack Agentic AI Engineer to join our growing AI team. The ideal candidate will have experience building end-to-end AI-powered applications, integrating LLMs, developing intelligent agents, and working across both frontend and backend technologies.

Responsibilities:

• Design, develop, and deploy AI-powered web applications using modern full-stack technologies.
• Build and optimise Agentic AI systems, autonomous workflows, AI assistants, and multi-agent frameworks.
• Integrate LLMs such as GPT, Claude, Gemini, Llama, or similar models into production applications.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and embeddings.
• Build scalable backend services, APIs, and microservices to support AI applications.
• Collaborate with Product, Data Science, and Engineering teams to translate business requirements into AI-driven solutions.
• Monitor, evaluate, and improve AI model performance, latency, and reliability.
• Implement prompt engineering, tool calling, memory management, and agent orchestration techniques.
• Ensure security, scalability, and maintainability of AI solutions.

The core requirements for the job include the following:

AI and Agentic AI:

• Hands-on experience with LLMs, Generative AI, and Agentic AI frameworks.
• Experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or similar frameworks.
• Strong understanding of RAG architectures, vector databases, embeddings, and prompt engineering.
• Experience integrating OpenAI, Anthropic, Gemini, or open-source LLMs.

Backend:

• Proficiency in Python.
• Experience with FastAPI, Flask, Django, or Node.js .
• Strong understanding of REST APIs, microservices, and cloud-native architectures.

Frontend:

• Experience with React.js, Next.js, TypeScript, JavaScript, HTML, and CSS.
• Ability to build intuitive AI-driven user interfaces and chat applications.

Data and Infrastructure:

• Experience with PostgreSQL, MongoDB, Redis, or similar databases.
• Familiarity with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.
• Exposure to Docker, Kubernetes, CI/CD pipelines, and cloud platforms (AWS, Azure, or GCP).

Preferred Qualifications:

• Experience building AI copilots, chatbots, AI assistants, or workflow automation platforms.
• Understanding of AI evaluation frameworks and observability tools.
• Knowledge of MLOps and model deployment practices.
• Experience working in a startup or fast-paced product environment.
• Bachelor's or Master's degree in Computer Science, Engineering, AI, or a related field.