AI Engineer

AI & Machine Learning Development

Generative AI & LLM Engineering

Data Engineering & Analytics

MLOps & Deployment

Cloud & Platform Engineering

Responsibilities

• Design, develop, and deploy machine learning and deep learning models.
• Build AI-powered solutions for prediction, recommendation, classification, and automation.
• Develop and optimize Generative AI applications using Large Language Models (LLMs).
• Fine-tune foundation models for domain-specific use cases.
• Develop applications using OpenAI, Azure OpenAI, Gemini, Claude, Llama, or similar models.
• Implement Retrieval-Augmented Generation (RAG) architectures.
• Design prompt engineering strategies and AI orchestration workflows.
• Build AI agents and conversational AI solutions.
• Evaluate model performance, bias, accuracy, and reliability.
• Collect, clean, process, and analyze large datasets.
• Design and implement data pipelines for AI workloads.
• Work with structured and unstructured data sources.
• Develop feature engineering and model training frameworks.
• Deploy AI models into production environments.
• Implement CI/CD pipelines for AI applications.
• Monitor model performance and drift.
• Establish model governance, versioning, and observability frameworks.
• Automate retraining and deployment processes.
• Build AI solutions on AWS, Azure, or Google Cloud Platform.
• Utilize AI/ML services such as Azure AI Services, SageMaker, Vertex AI, or equivalent platforms.
• Optimize model performance, scalability, and cost efficiency.
• Ensure secure deployment of AI applications.