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.

Experience
2-5 yrs