Python programming Machine Learning Algorithms (Regression, Classification, Clustering) Deep Learning frameworks: TensorFlow, PyTorch, Keras Data Analysis using Pandas, NumPy SQL and Database Management Data Visualization using Matplotlib, Seaborn, Power BI (optional) REST APIs and Model Deployment Version Control using Git

Develop, train, test, and deploy Machine Learning and Deep Learning models. Build AI-powered applications using NLP, Computer Vision, Generative AI, and predictive analytics techniques. Perform data collection, cleansing, feature engineering, and model optimization.

Evaluate model performance using appropriate metrics and improve accuracy. Work with structured and unstructured datasets for business problem-solving. Develop APIs and integrate ML models into production applications.

Collaborate with data scientists, software developers, and business stakeholders. Monitor model performance and maintain deployed AI solutions. Document model architecture, experiments, and deployment processes

Natural Language Processing (NLP) Computer Vision Generative AI / Large Language Models (LLMs) Prompt Engineering LangChain or similar AI orchestration frameworks Vector Databases (FAISS, Pinecone, ChromaDB)

📌 AI ML Engineer (Bengaluru)
🏢 Infosys
📍 Bengaluru