AI Engineer

Role overview

Responsibilities

• check_circle Design, develop, and deploy AI/ML models for real-world applications.
• check_circle Build and optimize generative AI applications using Large Language Models (LLMs).
• check_circle Develop Retrieval-Augmented Generation (RAG) pipelines and AI agents.
• check_circle Fine-tune and evaluate machine learning and deep learning models.
• check_circle Integrate AI models into web and mobile applications using APIs.
• check_circle Create scalable data pipelines for model training and inference.
• check_circle Monitor model performance and retrain models as needed.
• check_circle Collaborate with cross-functional teams to gather requirements and deliver AI solutions.
• check_circle Ensure AI systems meet security, privacy, and ethical AI standards.
• check_circle Stay updated with the latest AI frameworks, research, and industry trends.

Basic qualifications

• Strong programming skills in Python.
• Experience with TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of LLMs (GPT, Llama, Claude, Gemini, Mistral).
• Experience with LangChain, LlamaIndex, or similar AI frameworks.
• Understanding of RAG, vector databases (Pinecone, Chroma, FAISS, Weaviate), and embeddings.
• Knowledge of REST APIs and microservices.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Strong understanding of SQL and NoSQL databases.
• Knowledge of Git and software development best practices.

Preferred qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 2–5+ years of experience in AI/ML engineering or software development.
• Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Understanding of prompt engineering, AI agents, and model evaluation techniques.
• Familiarity with computer vision or natural language processing is a plus.