AIML Lead

Job Summary:

Machine Learning Lead

 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.

• 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.