AI Engineer- Bangalore

ai engineer An AI engineer is a software engineer who designs, builds, deploys, and maintains applications powered by artificial intelligence. The role combines programming, machine learning, data engineering, and software engineering to create AI systems that solve real-world problems. Typical responsibilities include: Building AI-powered applications (chatbots, recommendation systems, search, automation tools)Integrating large language models (LLMs) into productsTraining, fine-tuning, or evaluating machine learning modelsDesigning data pipelines and APIsDeploying AI models to production and monitoring their performanceOptimizing models for speed, cost, and accuracyCommon skills: Programming: Python, SQL, JavaScript (sometimes Java or C++)Machine Learning: Scikit-learn, XGBoost, TensorFlow, PyTorchGenerative AI: LLMs, prompt engineering, retrieval-augmented generation (RAG), AI agentsCloud Platforms: AWS, Google Cloud, AzureDatabases: PostgreSQL, MongoDB, vector databasesDevOps: Docker, Kubernetes, Git, CI/CDA typical learning path is: Learn Python and data structures.Study mathematics (linear algebra, probability, statistics).Learn machine learning fundamentals.Build projects with deep learning.Learn LLMs, RAG, AI agents, and model deployment.Create a portfolio on GitHub with real-world projects.Examples of AI engineer projects: Customer support chatbotAI document summarizerResume screening systemImage classification appVoice assistantAI coding assistant ai engineer An AI engineer is a software engineer who designs, builds, deploys, and maintains applications powered by artificial intelligence. The role combines programming, machine learning, data engineering, and software engineering to create AI systems that solve real-world problems. Typical responsibilities include: Building AI-powered applications (chatbots, recommendation systems, search, automation tools)Integrating large language models (LLMs) into productsTraining, fine-tuning, or evaluating machine learning modelsDesigning data pipelines and APIsDeploying AI models to production and monitoring their performanceOptimizing models for speed, cost, and accuracyCommon skills: Programming: Python, SQL, JavaScript (sometimes Java or C++)Machine Learning: Scikit-learn, XGBoost, TensorFlow, PyTorchGenerative AI: LLMs, prompt engineering, retrieval-augmented generation (RAG), AI agentsCloud Platforms: AWS, Google Cloud, AzureDatabases: PostgreSQL, MongoDB, vector databasesDevOps: Docker, Kubernetes, Git, CI/CDA typical learning path is: Learn Python and data structures.Study mathematics (linear algebra, probability, statistics).Learn machine learning fundamentals.Build projects with deep learning.Learn LLMs, RAG, AI agents, and model deployment.Create a portfolio on GitHub with real-world projects.Examples of AI engineer projects: Customer support chatbotAI document summarizerResume screening systemImage classification appVoice assistantAI coding assistant