Required Skills &
Qualifications:

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.

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 settings.
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.

Must Have:
Proficiency in Python
Libraries and Frameworks - TensorFlow, PyTorch, Keras &
Scikit-learn
Exp in Git, Jupitor Notebook
Data Maniplation Skills - Pandas, NumPy, SQL &
Data Wrangling
Machine Learning Concepts

📌 Ai Ml Engineer Bengaluru (India)
🏢 ALTEN
📍 India