
Machine Learning EngineerJR
4 days ago
Job Summary:
We are seeking a highly motivated and experienced Machine Learning Engineer with 3 to 5 years of hands-on experience to join our team. The ideal candidate will have strong foundations in computer science, including data structures, algorithms, and system design, along with practical experience developing and deploying scalable machine learning systems in production environments.
Position Summary Inclusion aims to simplify banking services by making it accessible and intuitive for consumers yet efficient for bankers.
Technical Skills
• 3+ years of experience in IT and 2+ years of relevant Machine learning experience
• Strong understanding of software engineering principles and fundamentals including data structures and algorithms.
• Excellent understanding of object-oriented concepts and Python.
• Strong knowledge of computer science fundamentals to develop a scalable system
• Experience in NLP models like BERT, Transformer architectures, etc.
• Experience in leveraging Computer Vision and OCR in document extraction use cases
• Familiarity with ML problems (ex, Classification/Regression/Anomaly Detection)
• Python ML Packages (Scikit/Numpy/Pandas/OpenCV)
• Exposure to REST API/ Flask concepts
• Experience in deep learning package, Pytorch, Tensorflow etc
Roles and Responsibilities
- Design, develop, and deploy scalable machine learning models and pipelines.
- Collaborate with data scientists, software engineers, and product teams to identify ML use cases and deliver end-to-end solutions.
- Optimize model performance, latency, and reliability for real-time or batch systems.
- Implement efficient data processing pipelines leveraging distributed computing technologies (e.g., Spark, Kafka, etc.).
- Write clean, maintainable, and well-tested code in languages such as Python, Java, or C++.
- Apply core computer science principles including data structures, algorithms, memory management, and object-oriented programming to build efficient solutions.
- Participate in design and code reviews, contribute to system architecture and infrastructure decisions.
- Monitor and troubleshoot deployed ML systems and continuously improve their performance.
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