Artificial Intelligence Engineer

Key Responsibilities
Design, develop, train, and deploy AI and machine learning models.
Build AI-powered applications using Python and relevant AI frameworks.
Work with Generative AI, LLMs, and prompt engineering.
Develop AI solutions using NLP, computer vision, and predictive analytics.
Integrate AI models with APIs, databases, and existing applications.
Implement Retrieval-Augmented Generation (RAG) pipelines and vector databases.
Prepare, clean, process, and analyze large datasets.
Evaluate model performance, accuracy, scalability, and reliability.
Deploy and monitor AI applications in cloud environments.
Collaborate with software engineers, data scientists, and product teams.
Follow best practices for AI security, responsible AI, and data privacy.
Required Technical Skills
Programming: Python, SQL, object-oriented programming.
Machine Learning: Scikit-learn, supervised and unsupervised learning.
Deep Learning: TensorFlow or PyTorch.
Generative AI: LLMs, prompt engineering, RAG, AI agents.
NLP: Text processing, embeddings, transformers.
Frameworks: LangChain, LangGraph, or similar tools.
Databases: SQL databases and vector databases such as FAISS, Pinecone, or Chroma.
APIs: REST APIs, FastAPI, or Flask.
Cloud & Deployment: AWS, Azure, or Google Cloud; Docker and CI/CD.
Version Control: Git and GitHub.
Preferred Qualifications
Experience building and deploying production-ready AI applications.
Knowledge of model fine-tuning and evaluation.
Familiarity with MLOps and model monitoring.
Understanding of data structures, algorithms, and software engineering principles.
A portfolio of AI projects on GitHub is an advantage.

Work arrangement
Yes