AI/ML Developer (ID 1445)

• 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.
  

Requirements

• 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.