
AIML Lead
13 hours ago
Job Summary:
We are looking for an experienced and visionary Machine Learning Lead with 8 to 12 years of industry experience to lead the design and development of advanced machine learning systems. The ideal candidate brings deep expertise in machine learning, computer science fundamentals, and system design, and is passionate about building robust, scalable, and intelligent systems that solve real-world problems at scale.
Position Summary : Platform for Financial Inclusion aims to simplify banking services by making it accessible and intuitive for consumers yet efficient for bankers.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related technical field.
- 8 to 12 years of experience in machine learning, software engineering, or related technical roles.
Technical Skills
• 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
Preferred Qualifications:
- Experience building and scaling real-time ML systems, such as recommendations, personalization engines, or fraud detection.
- Deep understanding of deep learning, NLP, time series forecasting, or reinforcement learning.
- Track record of leading cross-functional ML projects from idea to production.
- Experience implementing robust monitoring, alerting, and model retraining strategies in production environments.
- Published papers, open-source contributions, or speaking experience in ML/AI conferences.
Roles and Responsibilities
- Lead the design, development, and deployment of scalable and reliable ML systems across the organization.
- Drive end-to-end ownership of ML projects — from problem definition and data exploration to model training, validation, deployment, and monitoring.
- Collaborate cross-functionally with data scientists, engineers, product managers, and business stakeholders to define ML strategies and deliver impactful solutions.
- Apply deep knowledge of computer science fundamentals, including data structures, algorithms, distributed systems, and system architecture, to build high-performance ML infrastructure.
- Guide and mentor a team of machine learning engineers and data scientists; establish best practices for model development, testing, and deployment (MLOps).
- Evaluate and integrate new technologies, tools, and frameworks that improve system scalability, efficiency, and maintainability.
- Ensure high-quality, well-documented, and maintainable code and ML pipelines.
- Champion a culture of experimentation, performance tracking, and continuous learning.
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