Ai Ml Engineer (OCR)

Alten; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Alten in Bengaluru seeks a data science/ML engineer to build end-to-end ML solutions, from model development to deployment in cloud environments. You’ll collaborate with data engineers and software teams to productionize models and optimize performance in big data workflows.

The role requires strong Python, experience with TensorFlow/PyTorch/Keras, Pandas/NumPy/SQL, and hands-on use of Jupyter, Git, Docker, and cloud platforms (AWS/GCP/Azure) for deployment.

Qualifications

• Proficiency in Python
• Libraries and frameworks - TensorFlow, PyTorch, Keras & Scikit-learn
• Experience with Git and Jupyter Notebook
• Data manipulation skills - Pandas, NumPy, SQL & Data wrangling
• Knowledge of ML concepts including supervised, unsupervised, and reinforcement learning
• Experience with big data frameworks like Apache Spark and Hadoop
• Familiarity with NoSQL databases for unstructured data
• Communication of model performance and insights
• API development for model deployment
• SDLC and modern DevOps practices
• Testing and debugging for code quality and model performance

Skills

Python

Java/Scala

C++

Big data

Machine learning

Supervised learning

Unsupervised learning

Reinforcement learning

Tools

Jupyter Notebook

Git

Docker

Cloud platforms (AWS/GCP/Azure)

Job description

Required Skills & Qualifications:

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.

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.

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