Ai Ml Engineer

Experience: 1-2 years of experience.

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 environment 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.

Key skills: Tensorflow, SCALA, Python, Java, C++, RESTful API, Jupyter Notebook, Machine Learning, Scikit-Learn, NoSQL, Docker, AWS, Azure, Data Wrangling, Hadoop, Neural Networks, Numpy, Deep Learning, Apache Spark, SQL, Pytorch, GIT, Pandas, Keras, Spark.

Role: Machine Learning Engineer

Industry Type: IT Services & Consulting

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Data Science & Machine Learning

Education: UG: B.Tech / B.E. in Any Specialization