Role & responsibilities

Key Responsibilities

Programming Languages
Python, Java/Scala: Commonly used in big data frameworks like Apache Spark.
C++: Practical for high-performance applications, such as those needing faster execution times.

Libraries and Frameworks
TensorFlow, PyTorch, Keras & Scikit-learn

Data Manipulation and Analysis
Pandas, NumPy, SQL & Data Wrangling

Machine Learning Concepts
Supervised Learning: Understanding classification and regression models.
Unsupervised Learning: Techniques such as clustering and dimensionality reduction.
Reinforcement Learning: Familiarity with algorithms relevant to decision-making processes.

Neural Networks and Deep Learning
Convolutional Neural Networks (CNNs): For image processing tasks.
Recurrent Neural Networks (RNNs): For sequence data like time series and natural language.
Generative Models: Such as GANs (Generative Adversarial Networks) for generating recent data

Tools and Platforms
Jupyter Notebook:



An interactive setting for exploratory data analysis and development.
Git: Version control for managing codebases.
Docker: For containerization of applications, ensuring consistency across environments.
Cloud Platforms: Familiarity with AWS, Google Cloud, or Azure for deploying ML models.

Software Development Skills
APIs: Creating and consuming RESTful APIs for model deployment.
Software Development Lifecycle (SDLC): Understanding the phases of software development and methodologies (e.g., Agile, DevOps).
Testing and Debugging: Techniques for ensuring code quality and model performance.

Big Data Technologies
Hadoop: For distributed data processing.
Spark: For big data analytics and processing.
NoSQL Databases: Such as MongoDB or Cassandra for handling unstructured data.

📌 Ai Ml Engineer Bengaluru
🏢 Quest Global
📍 Bengaluru