Role & responsibilities

Key Responsibilities

Programming Languages

- Python, Java/Scala: Commonly used in big data frameworks like Apache Spark.

- C++: Useful 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 new 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