Develop and deploy intelligent applications using machine learning and generative AI technologies, including large language models (LLMs).

Build and integrate APIs using Python and frameworks such as FastAPI or Flask to incorporate AI capabilities into real-world products.

Apply machine learning fundamentals, including model training, inference, feature engineering, and model evaluation.

Work with LLM APIs such as OpenAI, Azure OpenAI, Claude, and Gemini to develop AI-powered applications.

Implement prompt engineering techniques and develop retrieval-augmented generation (RAG) systems.

Use frameworks such as LangChain and LlamaIndex to build LLM-based applications and work with basic agent frameworks such as LangGraph and CrewAI.

Process and analyse structured and unstructured datasets using Pandas and NumPy, and work with embeddings and vector search.

Integrate SQL and vector databases such as Pinecone, FAISS, Weaviate, or Chroma into AI applications.

Support application deployment using cloud platforms such as AWS, Azure, or GCP, and use Docker for containerisation.

Use Git, REST APIs, and Jupyter Notebook for development, testing, and collaboration.

Candidates should have strong programming skills in Python, while basic knowledge of JavaScript or TypeScript is optional.

Candidates should understand machine learning fundamentals, including model training, inference, feature engineering, and evaluation metrics.

Candidates should have experience working with LLM APIs, prompt engineering, and retrieval-augmented generation (RAG).

Candidates should be familiar with AI development frameworks such as LangChain or LlamaIndex, with a basic understanding of agent frameworks such as LangGraph or CrewAI.

Candidates should have experience handling structured and unstructured data using Pandas and NumPy, along with an understanding of embeddings and vector search.

Candidates should possess knowledge of SQL and familiarity with vector databases such as Pinecone, FAISS, Weaviate, or Chroma.

Candidates should have basic knowledge of at least one cloud platform, such as AWS, Azure, or GCP, and familiarity with Docker.

Candidates should be comfortable working with REST APIs, Git, and Jupyter Notebook.